Patent Publication Number: US-2020294682-A1

Title: Medical interview apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2019-045374, filed 2019 Mar. 13 and Japanese Patent Application No. 2020-042165, filed 2020 Mar. 11; the entire contents of all of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to a medical interview apparatus. 
     BACKGROUND 
     Before consulting a doctor at a hospital, a patient usually receives a previously prepared medical questionnaire at a hospital reception and enters his/her symptoms in the medical questionnaire. 
     Such a previously prepared medical questionnaire is created for an unspecified large number of patients, and the patient may have difficulty in correctly entering his/her real symptoms in the previously prepared medical questionnaire. Thus, a system that generates a new question item in response to a medical questionnaire answer and asks a patient again has been proposed. 
     Unfortunately, such a conventional system simply asks a symptom answered by the patient in more detail as the new question item and does not efficiently inquire the symptoms of the patient. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a system block diagram illustrating a configuration example of a medical interview system according to an embodiment; 
         FIG. 2  is a functional block diagram illustrating a functional configuration example of a user terminal and a doctor terminal; 
         FIG. 3  is a functional block diagram illustrating a functional configuration example of a server apparatus according to the embodiment; 
         FIG. 4  is a view illustrating an example of an initial medical questionnaire; 
         FIG. 5  is a view illustrating an example of correspondence relations between symptoms and disease names stored in a disease name symptom database; 
         FIG. 6  is a view illustrating an example of selecting symptoms desired to be additionally inquired; 
         FIG. 7  ( FIG. 7A  and  FIG. 7B ) is a view illustrating an example of an additional medical questionnaire; 
         FIG. 8  ( FIG. 8A  and  FIG. 8B ) is a view illustrating a display example of symptoms obtained by a medical interview, displayed on the doctor terminal by the server apparatus; 
         FIG. 9  is a view illustrating an example of medical interview information displayed on the doctor terminal by the server apparatus; 
         FIG. 10  is a flowchart illustrating an example of a processing flow of the medical interview system; and 
         FIG. 11  is a view for explaining a medical interview system  5  according to a second embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     According to one embodiment, a medical interview apparatus comprises processing circuitry. The processing circuitry is configured to receive an initial answer to an initial medical questionnaire inquiring a symptom; present an additional medical questionnaire inquiring a symptom related to the initial answer based on the initial answer; receive an additional answer to the additional medical questionnaire; and determine at least one first disease name based on the initial answer and the additional answer. 
     Embodiments of a medical interview apparatus and a medical interview system will be described below with reference to the drawings. 
     First Embodiment 
       FIG. 1  is a view illustrating a system configuration example of a medical interview system according to an embodiment. 
     Explanation of an Entire Configuration of the Medical Interview System 
     As illustrated in  FIG. 1 , a medical interview system  1  according to the present embodiment includes a server apparatus  2 , a doctor terminal  4 , and a user terminal  10 . The server apparatus  2  and the user terminal  10  are communicably connected to each other via a communication network N. The communication network N is, for example, the Internet or a private network such as a virtual private network (VPN). The server apparatus  2  and the doctor terminal  4  can directly or indirectly communicate with each other, for example, by a hospital local area network (LAN)  6  installed in a hospital. The user terminal  10 , which is possessed by an individual patient, may include a plurality of user terminals. The doctor terminal  4 , which is installed, for example, in an individual consultation room, may also include a plurality of doctor terminals  4  if there are a plurality of consultation rooms. 
     The server apparatus  2  receives an initial answer to an initial medical questionnaire inquiring symptoms from the user terminal  10 . The server apparatus  2  presents an additional medical questionnaire inquiring a symptom related to the initial answer to the user terminal  10  based on the initial answer. The server apparatus  2  receives an additional answer to the additional medical questionnaire from the user terminal  10 , and determines at least one first disease name based on the initial answer and the additional answer. The term “determine” in the present embodiment includes prediction using an AI model and identification by an arithmetic operation using a table. 
     The server apparatus  2  also transmits the initial medical questionnaire inquiring symptoms to the user terminal  10 . The server apparatus  2  receives an answer to the initial medical questionnaire from the user terminal  10 , and predicts at least one disease name based on a symptom answered in the initial medical questionnaire. The server apparatus  2  predicts another symptom from the predicted disease name, and generates the additional medical questionnaire inquiring a symptom to be asked again in accordance with the predicted other symptom. The server apparatus  2  may acquire an additional medical questionnaire in accordance with the answer to the initial medical questionnaire from additional medical questionnaires that have already been created. The server apparatus  2  then transmits the additional medical questionnaire to the user terminal  10 , and receives an answer to the additional medical questionnaire from the user terminal  10 . The server apparatus  2  further narrows down a disease name candidate based on the answer to the additional medical questionnaire. The server apparatus  2  is an example of the medical interview apparatus. The user terminal  10  is an example of a terminal apparatus. 
     The doctor terminal  4  is a terminal such as a personal computer installed, for example, in an individual consultation room. The doctor terminal  4  receives medical interview information (medical interview results) including the answers to the initial medical questionnaire and the additional medical questionnaire, and the predicted disease name predicted by the server apparatus  2  from the server apparatus  2 , and displays the received medical interview information. A doctor examines a target patient by referring to the medical interview information, and inputs medical record information of an electronic medical record, that is, the symptoms of the patient and the doctor&#39;s opinion from the doctor terminal  4 . 
     The user terminal  10  receives the initial medical questionnaire and the additional medical questionnaire from the server apparatus  2 . The user terminal  10  receives the answers to the initial medical questionnaire and the additional medical questionnaire from, for example, the patient. The user terminal  10  also transmits the answers to the initial medical questionnaire and the additional medical questionnaire to the server apparatus  2 . The user terminal  10  is, for example, a portable terminal such as a smartphone and a tablet personal computer (PC) possessed by the patient or his/her family. 
     Explanation of a Functional Configuration of the User Terminal and the Doctor Terminal 
       FIG. 2  is a functional block diagram illustrating a functional configuration example of the user terminal  10  and the doctor terminal  4 . As illustrated in  FIG. 2 , the user terminal  10  and the doctor terminal  4  are configured such that a controller  101 , a storage  102 , a communication interface (I/F)  103 , and an input/output (I/O) unit controller  104  are connected via a bus  105 . The user terminal  10  may be any device that at least has functions of displaying the medical questionnaire, filling out the medical questionnaire, and transmitting and receiving the medical questionnaire via a network or a communication line. The user terminal  10  may be, for example, a smartphone. The doctor terminal  4  may be any device that at least has functions of browsing the medical questionnaire and transmitting and receiving the medical questionnaire via a network or a communication line. The doctor terminal  4  may be, for example, a medical record terminal. 
     The controller  101  has functions as a processor and is composed of a central processing unit (CPU), a read-only memory (ROM), a random-access memory (RAM), or the like. The controller  101  controls each unit of the doctor terminal  4  or the user terminal  10  with the CPU reading a control computer program stored in the ROM or the storage  102 , loading the computer program into the RAM, and sequentially executing the computer program. The storage  102  is, for example, a hard disk drive, and stores the control computer program executed by the CPU of the controller  101  and various types of setting data. Examples of the control computer program stored in the storage  102  include a browser for intercommunication with the server apparatus  2 , and a file communication application for transmitting and receiving a medical questionnaire file to and from the server apparatus  2 . 
     The communication I/F  103  controls communication between the user terminal  10  and the server apparatus  2 , and between the doctor terminal  4  and the server apparatus  2 . To be more specific, the communication I/F  103  of the user terminal  10  performs data communication with the server apparatus  2  via the communication network N. The communication I/F  103  of the doctor terminal  4  performs data communication with the server apparatus  2  via a hospital LAN  6 . 
     The I/O unit controller  104  controls various I/O units under the control of the controller  101 . Examples of the I/O units controlled by the I/O unit controller  104  include an operation circuit  106 , such as a keyboard, a mouse and a touch panel, and a display  107 , such as a liquid crystal display. 
     In particular, in the user terminal  10 , the communication I/F  103  receives the initial medical questionnaire and the additional medical questionnaire from the server apparatus  2 . The communication I/F  103  transmits the answer (the initial answer) for the initial medical questionnaire and the answer (the additional answer) for the additional medical questionnaire to the server apparatus  2 . The communication I/F  103  is an example of a receiver and a transmitter. 
     In the user terminal  10 , the display  107  displays the initial medical questionnaire and the additional medical questionnaire. The controller  101  receives the answers to the initial medical questionnaire and the additional medical questionnaire from the patient. The controller  101  is an example of a receiver. 
     Explanation of a Functional Configuration of the Server Apparatus 
       FIG. 3  is a functional block diagram illustrating a functional configuration example of the server apparatus  2 . As illustrated in  FIG. 3 , the server apparatus  2  includes a controller  201  and a storage  220 . 
     The controller  201  has functions as a processor and is composed of a CPU, a ROM, a RAM, or the like. The controller  201  controls each unit of the server apparatus  2  with the CPU loading a control computer program P 1  stored in the ROM or the storage  220  into the RAM and sequentially executing the computer program. 
     By the CPU sequentially executing the control computer program P 1 , the controller  201  achieves, as functional parts, a first receiving function  202 , a disease name predicting function  203 , a symptom predicting function  204 , a medical questionnaire regenerating function  205 , a second receiving function  206 , a predicted disease name identifying function  207 , a medical interview information generating function  208 , a display control function  209  and a communication control function  210  as illustrated in  FIG. 3 . 
     The first receiving function  202  receives an answer to an initial medical questionnaire C 1  inquiring symptoms. To be more specific, the server apparatus  2  receives the completed initial medical questionnaire C 1  from the user terminal  10 . The first receiving function  202  may receive the initial medical questionnaire C 1  every time a user filling out the initial medical questionnaire C 1  operates, for example, a “Next” button to move to a next question item of the initial medical questionnaire C 1  in the user terminal  10 . When detecting activation of an application for filling out the initial medical questionnaire C 1  in the user terminal  10 , or when detecting input of a hospital name in the user terminal  10 , the server apparatus  2  determines that the medical questionnaire has been requested, and makes the communication control function  210  transmit the initial medical questionnaire C 1  to the user terminal  10 . 
     The disease name predicting function  203  predicts (or determines) at least one predicted disease name based on the answer received by the first receiving function  202 . The disease name predicting function  203  predicts at least one predicted disease name as a first or second disease name based on a symptom of a patient (the initial answer) entered in the initial medical questionnaire C 1  received by the first receiving function  202 . To be more specific, the disease name predicting function  203  predicts at least one predicted disease name by using a table in which an individual symptom and a disease name causing the symptom correspond to each other. The disease name predicting function  203  also predicts at least one predicted disease name by using a pre-trained model outputting at least one second disease name for at least one symptom input thereto. The disease name predicting function  203  is an example of a first or second determining part. 
     The symptom predicting function  204  predicts a symptom of the predicted disease name predicted by the disease name predicting function  203  based on the predicted disease name. When the disease name predicting function  203  predicts only one predicted disease name, the symptom predicting function  204  predicts another symptom possibly corresponding to the predicted disease name. 
     The medical questionnaire regenerating function  205  generates an additional medical questionnaire C 2  inquiring a symptom in accordance with the symptom predicted by the symptom predicting function  204 . The medical questionnaire regenerating function  205  also highlights the symptom predicted by the symptom predicting function  204  in the generated additional medical questionnaire C 2 . When the disease name predicting function  203  predicts only one predicted disease name, the medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  inquiring another symptom possibly corresponding to the predicted disease name. The medical questionnaire regenerating function  205  and the symptom predicting function  204  are an example of a first generating part. That is, the medical questionnaire regenerating function  205  and the symptom predicting function  204  generate the additional medical questionnaire by using a pre-trained model outputting a medical questionnaire for at least one disease name input thereto. The medical questionnaire regenerating function  205  and the symptom predicting function  204  also generate the additional medical questionnaire by an arithmetic operation using a table in which a plurality of disease names and a plurality of medical interviews correspond to each other. 
     When the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  including a question regarding a symptom predicted to have a highest onset possibility out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204 . 
     The medical questionnaire regenerating function  205  may classify the onset possibility, for example, into five levels from 1, which is lowest, to 5, which is highest, and add a question regarding a symptom having an onset possibility higher than a predetermined level to the additional medical questionnaire C 2 . 
     When the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  including a question regarding a symptom more shared among the predicted disease names out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204 . 
     When the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  including a question regarding a symptom less shared among the predicted disease names out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204 . 
     The medical questionnaire regenerating function  205  may acquire the additional medical questionnaire C 2  in accordance with the answer to the initial medical questionnaire C 1  from the additional medical questionnaires C 2  that have already been created. A specific example of the additional medical questionnaire C 2  will be described later ( FIG. 7A  and  FIG. 7B ). 
     The second receiving function  206  receives an answer to the additional medical questionnaire C 2  generated by the medical questionnaire regenerating function  205 . 
     The predicted disease name identifying function  207  identifies (or determines) the predicted disease name predicted by the disease name predicting function  203  as the first or second disease name based on the initial answer to the initial medical questionnaire and the symptom received as the additional answer by the second receiving function  206 . That is, the predicted disease name identifying function  207  identifies (or determines) the predicted disease name predicted by the disease name predicting function  203  as the first or second disease name by using a table in which an individual symptom and a disease name causing the symptom correspond to each other. The predicted disease name identifying function  207  also identifies (or determines) the predicted disease name predicted by the disease name predicting function  203  as the first or second disease name by using a pre-trained model outputting at least one disease name for at least one symptom input thereto. Additionally, when the disease name predicting function  203  predicts only one predicted disease name, the predicted disease name identifying function  207  determines whether the predicted disease name is developed based on the answer to the additional medical questionnaire C 2 . 
     The predicted disease name identifying function  207  is an example of the first or second determining part. 
     The medical interview information generating function  208  generates medical interview information  35  including the answer (the initial answer) for the initial medical questionnaire C 1 , the answer (the additional answer) for the additional medical questionnaire C 2 , and the disease name identified by the predicted disease name identifying function  207 . The medical interview information generating function  208  is an example of a second generating part. In the medical interview information  35 , the symptom characterizing the disease name identified by the predicted disease name identifying function  207  is highlighted. 
     The display control function  209  displays the initial medical questionnaire C 1  and the additional medical questionnaire C 2  on the display  107  of the user terminal  10 . The display control function  209  also displays the medical interview information  35  on the display  107  of the doctor terminal  4  to which a doctor inputs medical record information. 
     The communication control function  210  controls communication between the controller  201  and the storage  220 . The communication control function  210  also controls communication between the server apparatus  2  and the user terminal  10 , and between the server apparatus  2  and the doctor terminal  4 . 
     The storage  220  is, for example, a hard disk drive, and stores the control computer program P 1  executed by the CPU of the controller  201  and various types of data. The storage  220  also stores a disease name symptom database DB, a disease name prediction model M 1 , a symptom prediction model M 2 , and the initial medical questionnaire C 1 . 
     The control computer program P 1  executed by the controller  201  may be recorded as an installable or executable file in a computer-readable recording medium such as a compact disc read-only memory (CD-ROM), a flexible disk (FD), a compact disc-recordable (CD-R), and a digital versatile disc (DVD). 
     The control computer program P 1  may be stored in a computer connected to a network such as the Internet such that the control computer program P 1  is downloaded via the network. Alternatively, the control computer program P 1  may be delivered or distributed via a network such as the Internet. 
     The disease name symptom database DB is a database that manages disease names and symptoms of various diseases. The disease name symptom database DB is created based on medical examination information of doctors at an individual hospital or guidelines issued by a learned society or the like. 
     The disease names and the symptoms stored in the disease name symptom database DB are associated with each other. For example, an ID uniquely assigned to each disease name is associated with a symptom that is a chief complaint of the disease name in the disease name symptom database DB. An ID is also uniquely assigned to each symptom and is associated with a disease name causing the symptom. 
     The disease name prediction model M 1  is a model that outputs one or more disease names for at least one symptom. The disease name prediction model M 1  can be achieved by a first model (AI model) in which an individual symptom, a disease name causing the symptom, and information indicating certainty of association between the symptom and the disease name are stored corresponding to one another, and a second model that executes an arithmetic operation using a table in which an individual symptom and a disease name causing the symptom are stored corresponding to each other. 
     The disease name prediction model M 1  as the first model is a computer program achieving an AI model such as a neural network. That is, the disease name prediction model M 1  is, for example, a pre-trained model (such as a deep neural network) outputting one or more disease names for at least one symptom input thereto. The computer program achieving the disease name prediction model M 1  can be constructed by machine learning using a plurality of sets of training data having a combination of at least one symptom as a typical chief complaint of a patient having a certain disease name, and the disease name diagnosed by a doctor. 
     The disease name prediction model M 1  as the second model is an arithmetic computer program determining one or more disease names based on at least one symptom by using a previously created table defining a plurality of correspondence relations between disease names and symptoms. 
     Both of the first model and the second model of the disease name prediction model M 1  can be formed using, for example, the data stored in the disease name symptom database DB. The information indicating certainty of association between the symptom and the disease name may be calculated, for example, based on the number of samples used for the learning of the disease name prediction model M 1 . To be more specific, when M patients out of N patients having a symptom of “cough” are diagnosed as “pneumonia”, certainty of predicting pneumonia from cough is registered as M/N. The certainty is changed with update of the disease name prediction model M 1 . 
     The symptom prediction model M 2  is a model (information) that stores association of an individual disease name, a symptom corresponding to the disease name, and information indicating certainty of association between the disease name and the symptom. 
     The symptom prediction model M 2  is a model that outputs one or more symptoms for at least one disease name. The symptom prediction model M 2  can be achieved by a third model (AI model) in which an individual disease name, at least one symptom corresponding to a disease of the disease name, and information indicating certainty of association between the disease name and the symptom are stored corresponding to one another, and a fourth model that executes an arithmetic operation using a table in which an individual disease name and a symptom corresponding to a disease of the disease name are stored corresponding to each other. 
     The symptom prediction model M 2  as the third model is a computer program achieving an AI model such as a neural network. That is, the symptom prediction model M 2  may be, for example, a pre-trained model (such as a deep neural network) outputting one or more symptoms for at least one disease name input thereto. The computer program achieving the symptom prediction model M 2  can be constructed by machine learning using a plurality of sets of training data having a combination of a disease name diagnosed by a doctor and at least one symptom as a typical chief complaint of a patient having the disease name. 
     The symptom prediction model M 2  as the fourth model is an arithmetic computer program determining one or more symptoms based on at least one disease name by using a previously created table defining a plurality of correspondence relations between symptoms and disease names. 
     Both of the third model and the fourth model of the symptom prediction model M 2  can be formed using, for example, the data stored in the disease name symptom database DB. The information indicating certainty of association between the disease name and the symptom may be calculated, for example, based on the number of samples used for the learning of the symptom prediction model M 2 . To be more specific, when M patients out of N patients are diagnosed as “pneumonia” have a symptom of “cough”, certainty of predicting cough from pneumonia is registered as M/N. The certainty is changed with update of the symptom prediction model M 2 . 
     The disease name prediction model M 1  and the symptom prediction model M 2  may be created and updated by using existing clinical data, or by using data collected by operating the medical interview system  1  of the present embodiment, that is, correspondence relations between entry contents of medical questionnaires and actual diagnostic results. 
     The disease name prediction model M 1  and the symptom prediction model M 2  have a learning function. That is, when the disease name predicted by the server apparatus  2  is different from a disease name diagnosed by a doctor, the server apparatus  2  updates the disease name prediction model M 1  and the symptom prediction model M 2  by associating the symptom of the patient with the disease name diagnosed by the doctor by a model updating part not illustrated in  FIG. 3 . 
     The initial medical questionnaire C 1  is a medical questionnaire initially presented to the patient using the user terminal  10  and inquiring the symptoms of the patient. The initial medical questionnaire C 1  is described below in detail ( FIG. 4 ). 
     Explanation of the Initial Medical Questionnaire 
       FIG. 4  is a view illustrating an example of the initial medical questionnaire C 1 . The initial medical questionnaire C 1  includes symptom information  20 , check boxes  21 , and an input complete button  22 . The patient checks the initial medical questionnaire C 1  displayed on the display  107  of the user terminal  10 . The patient operates the operation circuit  106  (for example, a touch panel) to input a check mark  23  to a check box  21  corresponding to symptom information  20  related to his/her symptom out of the symptom information  20  displayed in the initial medical questionnaire C 1 . To be more specific, the controller  101  of the user terminal  10  displays the check mark  23  in the check box  21  touched by the patient. 
     After inputting all of his/her symptoms, the patient pushes the input complete button  22 . The controller  101  of the user terminal  10  detects that the input complete button  22  has been pushed, and transmits the initial medical questionnaire C 1  to the server apparatus  2 . 
     The initial medical questionnaire C 1  is previously created in order to comprehensively check the symptoms of patients, and is stored in the storage  220  of the server apparatus  2 . The initial medical questionnaire C 1  has question items regarding various physical symptoms so as to comprehensively check the symptoms of patients. 
     The initial medical questionnaire C 1  dedicated for a specific department (e.g., otolaryngology, ophthalmology, and orthopedics) may be prepared in the storage  220  for a patient who wants to consult a doctor in a specific department. 
     Although the initial medical questionnaire C 1  in  FIG. 4  is answered by inputting the check mark  23  to the check box  21 , the answer format is not limited to this method. That is, the patient may input his/her symptom by a word (for example, headache), or by a sentence (for example, I have a headache). 
     The patient may further input the degree of his/her symptom by a numerical value (for example, ten levels). The first receiving function  202  of the server apparatus  2  receiving the entry result of the initial medical questionnaire C 1  is configured to be able to receive the information input in accordance with the input format of the initial medical questionnaire C 1 . To be more specific, the server apparatus  2  has, for example, a text recognition function enabling reception of the answer using a word or a sentence. 
     Explanation of a Method of Predicting a Disease Name Candidate 
       FIG. 5  is a view illustrating an example of correspondence relations between symptoms and disease names stored in the disease name symptom database DB. The disease name symptom database DB stores symptoms  40  and suspected disease names  41  associated with each other. 
     The disease name predicting function  203  predicts the predicted disease name(s) by using the disease name prediction model M 1 . For example, the chief complaint of the patient in the initial medical questionnaire C 1  is assumed to be “chest pain” as illustrated in  FIG. 4 . In this case, the disease name predicting function  203  predicts a disease name(s) related to the symptom of “chest pain” as a possible disease name(s) from the column of the symptoms  40  by using the disease name prediction model M 1  created based on the disease name symptom database DB. 
     To be more specific, when the disease name prediction model M 1  is described in a table format, the disease name predicting function  203  reads the predicted disease name(s) related to the symptom of “chest pain” from the table. When the disease name prediction model M 1  is described as a mathematical model such as a neural network, the disease name predicting function  203  may predict the predicted disease name(s) based on an evaluation value corresponding to the onset possibility of the symptom of “chest pain”, output to an output layer of the neural network for the symptom of “chest pain” input thereto. At this point, the disease name predicting function  203  may output only one predicted disease name outputting a highest evaluation value as a most possible predicted disease name, or may output a plurality of predicted disease names outputting an evaluation value higher than a predetermined threshold value as possible predicted disease names. When the disease name prediction model M 1  has the certainty of association between the symptom and the predicted disease name, the predicted disease name(s) to be output may be determined by using the certainty. 
     In accordance with the example in  FIG. 5 , spontaneous pneumothorax, lung cancer, reflux esophagitis, angina, acute myocardial infarction, acute heart failure, acute myocarditis, bacterial pneumonia, pleurisy, Boerhaave syndrome, and acute pancreatitis are suspected as the predicted disease names for the symptom of “chest pain” hatched in the drawing. 
     In the present embodiment, spontaneous pneumothorax, lung cancer, angina, acute myocardial infarction, and bacterial pneumonia are assumed to be predicted as the possible predicted disease names out of the above 11 disease names. 
     Explanation of a Method of Predicting a Symptom Candidate 
     The symptom predicting function  204  predicts the symptom(s) by using the symptom prediction model M 2 . To be more specific, the symptom predicting function  204  predicts whether there is another symptom possibly corresponding to the predicted disease name(s) predicted by the disease name predicting function  203  for the answer in the initial medical questionnaire C 1 . 
     The symptom predicting function  204  may predict only one symptom having a highest onset possibility, or a plurality of symptoms having a high onset possibility. At this point, the symptom predicting function  204  possibly predicts the symptom chiefly complained of in the initial medical questionnaire C 1 , and a symptom not chiefly complained of in the initial medical questionnaire C 1 . The medical interview system  1  determines to ask the patient again whether the patient has the symptom not chiefly complained of in the initial medical questionnaire C 1  out of the symptoms predicted by the symptom predicting function  204 . 
     The symptom not chiefly complained of in the initial medical questionnaire C 1  includes a symptom not included in the questions in the initial medical questionnaire C 1  and a symptom included in the questions in the initial medical questionnaire C 1  but not answered. 
     The medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  including the symptom(s) desired to be asked again based on the symptom(s) predicted by the symptom predicting function  204 . A specific example of the additional medical questionnaire C 2  will be described later ( FIG. 7A  and  FIG. 7B ). 
       FIG. 6  is a view illustrating an example of selecting symptoms desired to be additionally inquired.  FIG. 6  specifically illustrates an example in which additional question items are selected based on the predicted disease names predicted when the patient complains of cough, fever, and chest pain in the initial medical questionnaire C 1 . 
     The symptom predicting function  204  predicts symptoms corresponding to the five predicted disease names predicted by the disease name predicting function  203 . To be more specific, the symptom predicting function  204  predicts a symptom not included in the questions in the initial medical questionnaire C 1  and a symptom included in the questions in the initial medical questionnaire C 1  but not answered out of the symptoms predicted from the predicted disease names predicted by the disease name predicting function  203 . 
       FIG. 6  illustrates the example in which the disease name predicting function  203  predicts spontaneous pneumothorax  41   a , lung cancer  41   b , angina  41   c , acute myocardial infarction  41   d , and bacterial pneumonia  41   e  as the predicted disease names as described using  FIG. 5 . 
     The symptom predicting function  204  predicts symptoms having a high onset possibility for the respective disease names by using the symptom prediction model M 2 . The symptom predicting function  204  selects one or a plurality of symptoms having a high onset possibility out of the predicted symptoms. 
     To be more specific, the symptom predicting function  204  may select a symptom having a highest onset possibility for each of the disease names. The symptom predicting function  204  may output only one symptom predicted to have a highest onset possibility, for example, by using the information indicating certainty of association between the disease name and the symptom stored in the symptom prediction model M 2 . 
     When the symptom prediction model M 2  is a neural network, the symptom predicting function  204  may predict the symptom(s) based on an evaluation value output to an output layer of the neural network for a specific disease name input thereto. At this point, the symptom predicting function  204  may output only one symptom outputting a highest evaluation value as the symptom having a highest onset possibility. 
     The symptom predicting function  204  may also output a plurality of symptoms having certainty higher than a predetermined threshold value as the symptoms having a high onset possibility by using the information indicating certainty of association between the disease name and the symptom stored in the symptom prediction model M 2 . The symptom predicting function  204  may output a plurality of symptoms outputting an evaluation value higher than a predetermined threshold value as the symptoms having a high onset possibility based on the evaluation value output to the output layer of the neural network. 
     Moreover, the symptom predicting function  204  may output a symptom shared among the disease name candidates as the symptom having a high onset possibility. 
     The symptom predicting function  204  may output a symptom input many times in the past to the initial medical questionnaire C 1  by the same patient as the symptom having a high onset possibility. 
     Additionally, the symptom predicting function  204  may output a symptom that enables the disease name to be more easily identified out of the symptoms predicted from the predicted disease names. 
     For example, the symptom predicting function  204  may output a symptom not shared among the disease names. The symptom predicting function  204  may also output a symptom input not many times in the past to the initial medical questionnaire C 1  by the same patient. 
     The symptom predicting function  204  may output a symptom suitable for distinguishing a correct disease name from a wrong disease name out of symptoms expected from both of the correct and wrong disease names if there was a wrong diagnosis in the past. The symptom predicting function  204  may also output a symptom predicted by referring to a past medical history of the patient to add the past medical history to each prediction described above. 
     The additional question items illustrated in  FIG. 6  are symptoms extracted as a result of each prediction described above. That is, shortness of breath, heart palpitations  40   a  are an example of the symptom more shared among the disease names. Each of weight loss  40   b , bone and joint pain  40   c , and bloody phlegm  40   d  is an example of the symptom corresponding only to the lung cancer  41   b , that is, the symptom less shared among the disease names. 
     For phlegm, it is desirable to inquire the condition of phlegm, for example, whether the patient has the bloody phlegm  40   d , and purulent phlegm  40   e  in order to identify the disease name. 
     Supplementary questions regarding the chest pain, that is, the pain ceases within 30 minutes  40   f , the pain lasts for longer than 30 minutes  40   g , and the pain is located in the upper abdomen  40   h  are examples of questions for inquiring the chest pain condition in more detail in order to identify the disease name. Such supplementary questions are also applied to a case, for example, in which a patient complaining of headache is asked about the headache condition (whether the entire headaches, whether a specific area of the headaches, what kind of pain the patient has, how long the patient has had the headache, or the like). 
     The medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  described below ( FIG. 7A  and  FIG. 7B ) based on the additional question items illustrated in  FIG. 6 . 
     Explanation of the Additional Medical Questionnaire 
       FIG. 7  ( FIG. 7A  and  FIG. 7B ) is a view illustrating an example of the additional medical questionnaire C 2 . To be more specific, the left side of  FIG. 7  ( FIG. 7A ) is a view illustrating an example of an additional medical questionnaire C 2   a , and the right side of  FIG. 7  ( FIG. 7B ) is a view illustrating an example of an additional medical questionnaire C 2   b.    
     The additional medical questionnaire C 2  follows the display format of the initial medical questionnaire C 1  for the sake of patient understandability. It is desirable to highlight the symptom(s) to be asked again, predicted by the symptom predicting function  204 . The symptom(s) may be highlighted in any manner, and may be hatched, underlined, displayed in a different color, displayed in a different font, or bolded. 
     The left side of  FIG. 7  ( FIG. 7A ) is an example of the additional medical questionnaire C 2   a  in which symptoms to be highlighted  24  are hatched. 
     As another style of highlighting, the symptoms to be asked again may be displayed above the symptom information  20  by rearranging the symptoms. An expected symptom, for example, “Do you have a headache?” may be additionally displayed at a specific position of a screen. 
     The medical questionnaire regenerating function  205  may also generate the additional medical questionnaire C 2   b  obtained by adding questions to the initial medical questionnaire C 1 . The right side of  FIG. 7  ( FIG. 7B ) is a view illustrating an example of the additional medical questionnaire C 2   b . The additional medical questionnaire C 2   b  includes an additional question area  25  added to the initial medical questionnaire C 1 . 
     Additional questions  26  and detailed questions  27  are displayed in the additional question area  25 . The additional questions  26  are questions determined to be asked again based on the prediction result of the symptom predicting function  204 . In the example of the right side of  FIG. 7  ( FIG. 7B ), the additional questions  26  include items included in the questions in the initial medical questionnaire C 1  but not answered, and an item not included in the initial medical questionnaire C 1 . Thus, the additional questions  26  may overlap with the items displayed in the initial medical questionnaire C 1 . The patient may tick a checkbox in the additional question area  25 , or tick the checkbox  21  of the item corresponding to the initial medical questionnaire C 1 . When one of the checkboxes of the additional question area  25  and the item corresponding to the initial medical questionnaire C 1  is ticked, the other checkbox may be ticked as well. 
     The detailed questions  27  are questions, for example, for obtaining more detailed information about a specific symptom. The questions for inquiring the chest pain condition in detail as described using  FIG. 6  are their examples. 
     When the patient completes the additional medical questionnaire C 2  (C 2   a , C 2   b ), the patient pushes the input complete button  22  similarly to the input of the initial medical questionnaire C 1 . The user terminal  10  thereby determines that the input of the medical questionnaire has been completed. 
     The input of the medical questionnaire may be forcibly terminated when the number of symptoms input to the additional medical questionnaire C 2  (or the initial medical questionnaire C 1 ) reaches a fixed number. The server apparatus  2  may receive the symptom input from the user terminal  10  and predict the disease name every time the symptom is input, and the input of the medical questionnaire may be forcibly terminated when the number of predicted disease name candidates reaches a fixed number. On the contrary, when the predicted disease name candidates are narrowed down to a fixed number or less, the input of the medical questionnaire may be terminated. 
     Explanation of a Method of Displaying a Symptom on the Doctor Terminal 
       FIG. 8  ( FIG. 8A  and  FIG. 8B ) is a view illustrating a display example of symptoms obtained by a medical interview, displayed on the doctor terminal  4  by the server apparatus  2 . 
     The medical interview information generating function  208  arranges the symptoms of the patient in such a form as to allow a medical staff (such as a doctor) who actually examines the patient to easily check the symptoms based on the answers to the initial medical questionnaire C 1  and the additional medical questionnaire C 2 . 
     The medical interview information generating function  208  generates noticeable symptom information  28  of the patient. The noticeable symptom information  28  is displayed such that the medical staff easily checks the symptoms characterizing the disease name(s) when examining the patient. The left side of  FIG. 8  ( FIG. 8A ) illustrates noticeable symptom information  28   a  as an example of the noticeable symptom information  28 . The noticeable symptom information  28   a  is an example of highlighting the symptoms characterizing the disease name(s) by displaying the symptoms in descending order of the onset possibility. 
     To be more specific, the left side of  FIG. 8  ( FIG. 8A ) illustrates the noticeable symptom information  28   a  having a high possibility of characterizing “lung cancer” that is re-predicted (or determined) by the predicted disease name identifying function  207  based on the answer to the additional medical questionnaire C 2 . That is, the left side of  FIG. 8  ( FIG. 8A ) is an example in which the symptoms are rearranged and displayed in descending order of the onset possibility during “lung cancer” based on the answers to the initial medical questionnaire C 1  and the additional medical questionnaire C 2 . When the patient develops “lung cancer”, the symptoms complained of by the patient have a decreasing possibility of characterizing lung cancer in the order of “bloody phlegm”&gt;“weight loss”&gt;“chest pain”&gt;“shortness of breath, heart palpitations”&gt;“cough”. 
     When the predicted disease name identifying function  207  selects a plurality of disease names upon identifying the disease name based on the answer to the additional medical questionnaire C 2 , the noticeable symptom information  28   a  may be displayed by ordering the symptoms from a symptom most shared or least shared among the disease name candidates. 
     Additionally, the noticeable symptom information  28   a  may be displayed by rearranging the symptoms in descending order of the number of inputs to the initial medical questionnaire C 1  and the additional medical questionnaire C 2 . 
     The right side of  FIG. 8  ( FIG. 8B ) illustrates noticeable symptom information  28   b  as an example of the noticeable symptom information  28 . The noticeable symptom information  28   b  is an example of highlighting the symptoms having a high possibility of characterizing “lung cancer” by hatching when the predicted disease name identifying function  207  identifies the disease name based on the answer to the additional medical questionnaire C 2 , and determines that the disease name is likely to be “lung cancer”. The highlighting method is not limited to hatching, and the symptoms may be underlined, displayed in a different color, displayed in a different font, or bolded. 
     Explanation of the Medical Interview Information Displayed on the Doctor Terminal 
       FIG. 9  is a view illustrating an example of the medical interview information  35  displayed on the doctor terminal  4  by the server apparatus  2 . The medical interview information  35  includes the noticeable symptom information  28 , a past medical history  29 , disease name candidates  30 , past medical examination records  31 , vital signs  32 , and diagnostic records  33 . 
     The noticeable symptom information  28  has been described above. In  FIG. 9 , the symptoms characterizing the predicted disease name candidates  30  are highlighted in descending order of the onset possibility. The past medical history  29  is a history of past illness of the patient. The past medical history can be acquired, for example, by an identification number for identifying an individual patient. The disease name candidates  30  are predicted from the initial medical questionnaire C 1  and the additional medical questionnaire C 2 .  FIG. 9  shows that the patient possibly develops lung cancer or pneumonia. For the disease name candidates, only one disease name having a highest possibility may be displayed, or a plurality of disease names having a high possibility may be displayed. 
     The past medical examination records  31  are information (e.g., a past medical history, past medication information/test information) recorded in electronic medical records during past medical examinations. The vital signs  32  are medical information (e.g., blood test results) related to the disease name(s) (lung cancer and pneumonia in the example of  FIG. 9 ) selected from the disease name candidates  30 . The diagnostic records  33  are past diagnostic records of the patient related to the disease name candidates  30 . The diagnostic records  33  are acquired from the electronic medical record as information related to the ticked disease names of the disease name candidates  30  and are displayed in the columns of the diagnostic records  33  in  FIG. 9  when an information acquisition button provided close to the disease name candidates  30  is pushed. The diagnostic records  33  in  FIG. 9  are medical images of the patient captured in the past as one example. 
     The doctor examines the patient by referring to the medical interview information  35  and diagnoses the disease name of the patient. If the disease names predicted as the disease name candidates  30  in the medical interview information  35  are different from the actual disease name determined by the diagnosis, the medical interview system  1  determines that it made a wrong prediction. 
     At this point, the server apparatus  2  associates the symptom information input to the initial medical questionnaire C 1  and the additional medical questionnaire C 2  with the disease name diagnosed by the doctor. The server apparatus  2  then updates the disease name prediction model M 1  and the symptom prediction model M 2  by using the associated data as new learning data. The learning of the disease name prediction model M 1  and the symptom prediction model M 2  is desirably performed by giving higher contribution to learning data indicating a relation between the symptom information of the patient obtained by the present diagnosis and the disease name diagnosed by the doctor than other learning data. The learning may be also performed by increasing the contribution of learning data of a disease name group in which the medical interview system  1  made a wrong prediction to the disease name prediction model M 1  and the symptom prediction model M 2  as compared to that of learning data of other disease name groups. 
     Explanation of a Processing Flow of the Medical Interview System 
       FIG. 10  is a flowchart illustrating an example of a processing flow of the medical interview system  1 . A processing flow of the user terminal  10 , a processing flow of the server apparatus  2 , and a processing flow of the doctor terminal  4  will be sequentially described below. 
     Explanation of the Processing Flow of the User Terminal 
     A patient activates an application for executing a medical interview in his/her user terminal  10  and requests a medical questionnaire for the server apparatus  2  (step S 10 ). The patient may input patient information such as a patient name when requesting the medical questionnaire. 
     The controller  101  of the user terminal  10  determines whether the communication I/F  103  receives the initial medical questionnaire C 1  from the server apparatus  2  (step S 11 ). When the controller  101  determines that the initial medical questionnaire C 1  has been received (Yes at step S 11 ), the process proceeds to step S 12 . When the controller  101  does not determine that the initial medical questionnaire C 1  has been received (No at step S 11 ), step S 11  is repeated. 
     The controller  101  receives input to the initial medical questionnaire C 1  from the patient (step S 12 ). 
     The controller  101  determines whether the input complete button  22  is pushed by working with the operation circuit  106  (step S 13 ). When the controller  101  determines that the input complete button  22  has been pushed (Yes at step S 13 ), the process proceeds to step S 14 . When the controller  101  does not determine that the input complete button  22  has been pushed (No at step S 13 ), the process returns to step S 12 . 
     When determining that the input complete button  22  has been pushed at step S 13 , the controller  101  makes the communication I/F  103  transmit the initial medical questionnaire C 1  to the server apparatus  2  at the subsequent step S 14 . 
     The controller  101  then determines whether the communication I/F  103  receives the additional medical questionnaire C 2  from the server apparatus  2  (step S 15 ). When the controller  101  determines that the additional medical questionnaire C 2  has been received (Yes at step S 15 ), the process proceeds to step S 16 . When the controller  101  does not determine that the additional medical questionnaire C 2  has been received (No at step S 15 ), step S 15  is repeated. 
     The controller  101  receives input to the additional medical questionnaire C 2  from the patient (step S 16 ). 
     The controller  101  determines whether the input complete button  22  is pushed by working with the operation circuit  106  (step S 17 ). When the controller  101  determines that the input complete button  22  has been pushed (Yes at step S 17 ), the process proceeds to step S 18 . When the controller  101  does not determine that the input complete button  22  has been pushed (No at step S 17 ), the process returns to step S 16 . 
     When determining that the input complete button  22  has been pushed at step S 17 , the controller  101  makes the communication I/F  103  transmit the additional medical questionnaire C 2  to the server apparatus  2  at the subsequent step S 18 . The user terminal  10  then terminates the process in  FIG. 10 . 
     Explanation of the Processing Flow of the Server Apparatus 
     The first receiving function  202  determines whether the user terminal  10  requests the medical questionnaire (step S 20 ). The server apparatus  2  may acquire, for example, the patient information from the user terminal  10  together with the request for the medical questionnaire at step S 20 . When the first receiving function  202  determines that the user terminal  10  has requested the medical questionnaire (Yes at step S 20 ), the process proceeds to step S 21 . When the first receiving function  202  does not determine that the user terminal  10  has requested the medical questionnaire (No at step S 20 ), step S 20  is repeated. 
     When determining that the user terminal  10  has requested the medical questionnaire at step S 20 , the first receiving function  202  makes the communication control function  210  transmit the initial medical questionnaire C 1  to the user terminal  10  at the subsequent step S 21 . 
     The first receiving function  202  then determines whether the initial medical questionnaire C 1  is received from the user terminal  10  (step S 22 ). When the first receiving function  202  determines that the initial medical questionnaire C 1  has been received from the user terminal  10  (Yes at step S 22 ), the process proceeds to step S 23 . When the first receiving function  202  does not determine that the initial medical questionnaire C 1  has been received from the user terminal  10  (No at step S 22 ), step S 22  is repeated. 
     The disease name predicting function  203  predicts the disease name(s) based on the entry contents of the initial medical questionnaire C 1  (step S 23 ). 
     The symptom predicting function  204  predicts the symptom(s) from the disease name(s) predicted at step S 23  (step S 24 ). 
     The medical questionnaire regenerating function  205  generates the additional medical questionnaire C 2  based on the symptom(s) predicted at step S 24  (step S 25 ). 
     The medical questionnaire regenerating function  205  makes the communication control function  210  transmit the additional medical questionnaire C 2  to the user terminal  10  (step S 26 ). 
     The predicted disease name identifying function  207  determines whether the additional medical questionnaire C 2  is received from the user terminal  10  (step S 27 ). When it is determined that the additional medical questionnaire C 2  has been received from the user terminal  10  (Yes at step S 27 ), the process proceeds to step S 28 . When it is not determined that the additional medical questionnaire C 2  has been received from the user terminal  10  (No at step S 27 ), step S 27  is repeated. 
     When it is determined that the additional medical questionnaire C 2  has been received from the user terminal  10  at step S 27 , the predicted disease name identifying function  207  re-predicts the disease name(s) of the patient at the subsequent step S 28 . 
     Subsequently, the medical interview information generating function  208  generates the medical interview information  35  (step S 29 ). 
     The medical interview information generating function  208  then makes the communication control function  210  transmit the medical interview information  35  to the doctor terminal  4  (step S 30 ). After that, the server apparatus  2  terminates the process in  FIG. 10 . 
     Explanation of the Processing Flow of the Doctor Terminal 
     The controller  101  of the doctor terminal  4  determines whether the medical interview information  35  is received (step S 40 ). When the controller  101  determines that the medical interview information  35  has been received (Yes at step S 40 ), the process proceeds to step S 41 . When the controller  101  does not determine that the medical interview information  35  has been received (No at step S 40 ), step S 40  is repeated. 
     When determining that the medical interview information  35  has been received at step S 40 , the controller  101  of the doctor terminal  4  displays the medical interview information  35  on the display  107  (step S 41 ). The doctor terminal  4  then terminates the process in  FIG. 10 . 
     As described above, in the server apparatus  2  (the medical interview apparatus) of the embodiment, the first receiving function  202  receives the answer to the initial medical questionnaire C 1  inquiring the symptoms. The disease name predicting function  203  predicts at least one disease name based on the symptom(s) received by the first receiving function  202 . The symptom predicting function  204  predicts the symptom(s) of the disease name(s) predicted by the disease name predicting function  203  based on the predicted disease name(s). The medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire C 2  inquiring the symptom(s) to be asked again in accordance with the symptom(s) predicted by the symptom predicting function  204 . The second receiving function  206  receives the answer to the additional medical questionnaire C 2  generated by the medical questionnaire regenerating function  205 . The predicted disease name identifying function  207  identifies the predicted disease name(s) predicted by the disease name predicting function  203  based on the symptom(s) received by the second receiving function  206 . That is, the medical interview is performed again using the additional medical questionnaire C 2  generated in accordance with the answer to the initial medical questionnaire C 1  of the patient. Thus, the disease condition of the patient can be efficiently collected. The patient can fill out the initial medical questionnaire C 1  and the additional medical questionnaire C 2  before arriving at the hospital. Thus, a waiting time at the hospital can be reduced. Moreover, the medical staff do not need to collect the symptom information of the patient at the hospital. 
     The medical interview information generating function  208  may guide the user terminal  10  which department to visit based on the result of the additional medical questionnaire C 2 . This is achieved by registering a department associated with an individual disease name in the above disease name symptom database DB. The medical interview information generating function  208  may also guide the user terminal  10  to call an ambulance based on the result of the additional medical questionnaire C 2  if the patient has emergent symptoms. 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the disease name predicting function  203  predicts the predicted disease name(s) by using the disease name prediction model M 1  storing the association between an individual symptom and a disease name causing the symptom. Thus, the disease name(s) can be easily and efficiently predicted. By updating the disease name prediction model M 1 , the disease name predicting function  203  can easily respond to a newly registered disease name. 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the symptom predicting function  204  predicts the symptom(s) by using the symptom prediction model M 2  storing the association between an individual disease name and a symptom corresponding to the disease name. Thus, the symptom(s) can be easily and efficiently predicted. By updating the symptom prediction model M 2 , the symptom predicting function  204  can easily respond to a newly registered symptom. 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the symptom prediction model M 2  is the information storing the association of an individual disease name, a symptom corresponding to the disease name, and information indicating certainty of association between the disease name and the symptom. When the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire including a question regarding a symptom predicted to have a highest onset possibility out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204  based on the symptom prediction model M 2 . Thus, the disease name candidates can be narrowed down by an answer to the question in the additional medical questionnaire C 2 . 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, when the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire C 2  including a question regarding a symptom more shared among the predicted disease names out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204 . Thus, the disease name candidate having a high possibility can be selected by an answer to the question in the additional medical questionnaire C 2 . 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, when the disease name predicting function  203  predicts a plurality of predicted disease names, the medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire C 2  including a question regarding a symptom less shared among the predicted disease names out of symptoms predicted from the respective predicted disease names by the symptom predicting function  204 . Thus, the disease name candidates can be narrowed down by an answer to the question in the additional medical questionnaire C 2 . 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire C 2  in which the symptom(s) predicted by the symptom predicting function  204  is highlighted. Thus, the patient easily notices the symptom(s) when answering the additional medical questionnaire C 2 . 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the medical interview information generating function  208  generates the medical interview information  35  including the answers to the initial medical questionnaire C 1  and the additional medical questionnaire C 2 , and the disease name(s) identified by the predicted disease name identifying function  207 . The display control function  209  displays the medical interview information  35  on the doctor terminal  4  to which the doctor inputs the medical record information. Thus, the doctor examining the patient can easily check the medical interview result of the patient. 
     In the server apparatus  2  (the medical interview apparatus) of the embodiment, the medical interview information  35  highlights the symptom(s) characterizing the diseased name(s) identified by the predicted disease name identifying function  207 . Thus, the doctor examining the patient can more easily check the medical interview result of the patient. This improves the efficiency of the medical examination. 
     The medical interview system  1  of the embodiment allows the server apparatus  2  and the user terminal  10  to work together, thereby collecting the symptoms of the patient. To be more specific, in the server apparatus  2 , the first receiving function  202  receives the answer to the initial medical questionnaire C 1  inquiring the symptoms. The disease name predicting function  203  predicts at least one predicted disease name based on the symptom(s) received by the first receiving function  202 . The symptom predicting function  204  predicts the symptom(s) of the disease name(s) predicted by the disease name predicting function  203  based on the predicted disease name(s). The medical questionnaire regenerating function  205  (the generating part) generates the additional medical questionnaire C 2  inquiring the symptom(s) to be asked again in accordance with the symptom(s) predicted by the symptom predicting function  204 . The second receiving function  206  receives the answer to the additional medical questionnaire C 2  generated by the medical questionnaire regenerating function  205 . The predicted disease name identifying function  207  identifies the predicted disease name(s) predicted by the disease name predicting function  203  based on the symptom(s) received by the second receiving function  206 . At this point, in the user terminal  10 , the communication I/F  103  (the receiver) receives the initial medical questionnaire C 1  and the additional medical questionnaire C 2  from the server apparatus  2 . The display  107  displays the initial medical questionnaire C 1  and the additional medical questionnaire C 2 . The controller  101  (the receiver) receives the answers to the initial medical questionnaire C 1  and the additional medical questionnaire C 2 . The communication I/F  103  (the transmitter) transmits the answers to the initial medical questionnaire C 1  and the additional medical questionnaire C 2  to the server apparatus  2 . Thus, the disease condition of the patient can be efficiently collected. 
     Second Embodiment 
     Next, a medical interview apparatus and a medical interview system according to a second embodiment will be described. A typical electronic medical record describes information such as a past medical history of a patient, a history of present illness, and a medicine that the patient regularly takes. The medical interview apparatus and the medical interview system according to the second embodiment generate a more accurate additional medical questionnaire based on the description in the electronic medical record and the disease name(s) predicted from the answer to the initial medical questionnaire. 
       FIG. 11  is a view for explaining a medical interview system  5  according to the second embodiment. The medical interview system  5  differs from the medical interview system  1  of the first embodiment in  FIG. 1  in further including a clinical information database  3 . 
     As illustrated in  FIG. 11 , the clinical information database  3  includes an electronic medical record database  3   a , and an image database  3   b . Clinical information means the information described in the electronic medical record, information related to the electronic medical record (e.g., nursing records, meal information, and medical payment information), image data, or the like, and is managed for an individual patient. 
     The electronic medical record database  3   a  is a database storing and managing an electronic medical record and information related to the electronic medical record for an individual patient. The electronic medical record database  3   a  of the present embodiment includes a database as a hospital information system (HIS). 
     The image database  3   b  is a database storing and managing image data acquired by various modalities and information related to the image data for an individual patient. The image database  3   b  of the present embodiment includes a database as a picture archiving and communication system (PACS) and a radiology information system (RIS). 
     The server apparatus  2  acquires the information described in the electronic medical record of the target patient from the electronic medical record database  3   a  via a network based on the patient information received from the user terminal  10 . To be more specific, the symptom predicting function  204  of the server apparatus  2  acquires the information described in the electronic medical record of the target patient from the electronic medical record database  3   a  via a network based on the patient information received from the user terminal  10 . 
     The information described in the electronic medical record of the patient, acquired by the symptom predicting function  204  of the server apparatus  2  is, for example, the past medical history of the patient, the history of present illness, the medicine that the patient regularly takes, and information regarding medical interviews performed in the past for the patient by doctors (medical interview contents and corresponding answers of the patient), described in the electronic medical record. 
     The symptom predicting function  204  of the server apparatus  2  predicts at least one symptom based on the disease name(s) predicted by the disease name predicting function  203 , and the information described in the electronic medical record of the patient, acquired from the electronic medical record database  3   a , by using the symptom prediction model M 2 . 
     The symptom prediction model M 2  is a computer program achieving an AI model such as a neural network. That is, the symptom prediction model M 2  is, for example, a pre-trained model (such as a deep neural network) outputting one or more symptoms for at least one disease name and the information described in the electronic medical record of the patient, input thereto. The computer program achieving the symptom prediction model M 2  can be constructed by machine learning using a plurality of sets of training data having a combination of a disease name diagnosed by a doctor, information described in an electronic medical record, and at least one symptom as a typical chief complaint of a patient having the disease name. The symptom predicting function  204  of the server apparatus  2  may predict at least one symptom based on the initial medical questionnaire as well if necessary. 
     The medical questionnaire regenerating function  205  of the server apparatus  2  generates the additional medical questionnaire C 2  based on the at least one symptom predicted by the symptom predicting function  204 . The medical questionnaire regenerating function  205  of the server apparatus  2  may generate the additional medical questionnaire C 2  in which information overlapped with that of the initial medical questionnaire C 1  is distinguished in information included in the additional medical questionnaire C 2  by referring to the initial medical questionnaire C 1 . 
     The symptom predicting function  204  and the medical questionnaire regenerating function  205  have been described as separate elements in the present embodiment. The symptom predicting function  204  and the medical questionnaire regenerating function  205  may be achieved by one pre-trained model (such as a deep neural network). Such a pre-trained model can be constructed by machine learning using a plurality of sets of training data where a disease name diagnosed by a doctor, information described in an electronic medical record, and the initial medical questionnaire C 1  are input data, and at least one symptom not overlapped with those of the initial medical questionnaire C 1  is output data (training data). 
     As described above, in the server apparatus  2  as the medical interview apparatus according to the present embodiment, the symptom predicting function  204  predicts at least one symptom based on the disease name(s) predicted by the disease name predicting function  203  and the information described in the electronic medical record of the patient, acquired from the electronic medical record database  3   a . The medical questionnaire regenerating function  205  of the server apparatus  2  generates the additional medical questionnaire C 2  based on the at least one symptom predicted by the symptom predicting function  204 . Thus, the server apparatus  2  can more accurately generate the additional medical questionnaire C 2  based on the information described in the electronic medical record of the patient as well. The server apparatus  2  can also more accurately predict the disease name(s) by using the accurate additional medical questionnaire C 2  based on the electronic medical record of the patient. 
     First Modification 
     The second embodiment employs the example in which the symptom predicting function  204  of the server apparatus  2  uses the information described in the electronic medical record of the patient, acquired from the electronic medical record database  3   a  when predicting at least one symptom. The information described in the electronic medical record of the patient, acquired from the electronic medical record database  3   a  can be also used as necessary, for example, when the disease name predicting function  203  predicts the disease name(s), or when the predicted disease name identifying function  207  identifies the disease name(s). 
     Second Modification 
     The above respective embodiments employ the example in which the server apparatus  2  is installed in a hospital. Other than the hospital, the server apparatus  2  may be also installed in any place that allows the server apparatus  2  to communicate with the user terminal  10  and the doctor terminal  4  via a network. For example, the server apparatus  2  can be installed as a server on a cloud. 
     Third Modification 
     The above respective embodiments employ the example in which the server apparatus  2  of the medical interview system  1  functions as the medical interview apparatus. The medical interview system  1  may be also achieved, for example, by installing, in the user terminal  10 , an application for executing the processing executed in the server apparatus  2 . 
     The term “processor” used in the above description refers to, for example, central processing units (CPUs), graphics processing units (GPUs), or circuits such as application specific integrated circuits (ASICs) and programmable logic devices including simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs). When the processor is, for example, a CPU, the processor reads and executes computer programs stored in a storage circuit to achieve the functions. When the processor is, for example, an ASIC, the functions are directly incorporated into the circuit of the processor as a logic circuit instead of storing computer programs in a storage circuit. The respective processors of the embodiments do not necessarily have to be configured as a single circuit individually. A plurality of independent circuits may be combined to form a single processor so as to achieve the functions. Additionally, the plurality of constituent elements in  FIG. 3  may be integrated into one processor to achieve the functions. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.