Patient Analytics Directed to East Asian Medicine

A system, method, and computer-readable medium are disclosed that provide patient analytics based East Asian Medicine (EAM) or Oriental Medicine (OM) or principles. Data specific to a patient along with environmental data related to the patient are collected. The collected data is processed using EAM principles through Artificial Intelligence/Machine Learning (AI/ML). The collected data is processed with EAM principles and output form AI/ML to provide patient health status over different time intervals. Diagnostics and recommendations are provided based on the AI/ML and symptoms checking.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to the implementation of information handling systems directed to East Asian Medicine. More specifically, embodiments of the invention provide a system, method, and computer-readable medium for gathering data, performing machine learning and providing patient analytics based on East Asian Medicine or Oriental Medicine.

Description of the Related Art

East Asian Medicine, also known as Oriental Medicine, is based on thousands of years of practice and theories. The practice of East Asian Medicine involves determining multivariate factors that are dynamic and ever-changing depending on time and environment. Practitioners rely on years of training, clinical practice, and accurate data collection and processing in determining the condition, diagnosis, and treatment of patients.

Because of the dynamic and ever changing nature of factors that affect a patient, and particularities specific to a patient, determining accurate and timely diagnostics and recommendations can be a challenge. To effectively provide accurate results, a skilled practitioner considers all related factors, determines relative environment and time, while typically performing manual calculations and diagnosis based on the practitioner's experience.

SUMMARY OF THE INVENTION

A system, method, and computer-readable medium are disclosed for providing patient analytics based on East Asian Medicine (EAM) comprising collecting specific data related to a patient and environmental data related to the patient; processing the collected data with EAM principles through Artificial Intelligence/Machine Learning (AI/ML) to provide an AI/ML output; processing the collected data, EAM principles, and AI/ML output to provide symptoms checking for the patient that includes health states over different time intervals; and providing diagnostics and recommendations based on the AI/ML output and symptoms checking.

DETAILED DESCRIPTION

A system, method, and computer-readable medium are disclosed for gathering data, performing machine learning and providing patient analytics based on East Asian Medicine (EAM) or also known as Oriental Medicine (OM). The principles of EAM/OM are considered to provide medicinal diagnostics and recommendation based on space (environment) and time specific to a patient. Consideration is accounted for as to organ rate variability, and changes as to weather, health and climate. Various sources, including weather related sources, patient specific data (current and historical), patient demographic data, patient birthdate, etc. are provided as input to various machine learning engines. Implementations of the machine learning engines include convolutional neural networks.

FIG.1is a generalized illustration of a user information handling system100that can be used to implement the system and method of the present invention. The information handling system100includes a processor (e.g., central processor unit or “CPU”)102, input/output (I/O) devices104, such as a keyboard, a video/display, a mouse, and associated controllers (e.g., K/V/M), a hard drive or disk storage106, and various other subsystems108. In various embodiments, the information handling system100also includes network port110operable to connect to a network140, which is likewise accessible by a service provider server142. The information handling system100likewise includes system memory112, which is interconnected to the foregoing via one or more buses114. System memory112further comprises operating system (OS)116and in various embodiments may also include application(s)118that are configured to perform various operations and tasks on the information handling system100.

Various implementations provide for applications118to include an OM application120, an herbal application122and an e-commerce lifestyle application124which interact with other elements as further described here.

FIG.2is a simplified block diagram of an East Asian Medicine (EAM) data gathering, machine learning, diagnostic, and recommendation system200. In various embodiments, the system200provides for patient(s)202through patient devices204to connect to the network140.

The network140may be a public network, such as the Internet, a physical private network, a wireless/wired network, a virtual private network (VPN), or any combination thereof. For example, the network140can include 5G wireless networks, and future evolutions of 5G. 5G and future standards can provide for the ability to seamlessly connect devices, such as patient devices200and embedded sensors in and connected to patient devices200, and can scale down in data rates, power, and mobility providing efficient and low-cost connectivity solutions. Skilled practitioners of the art will recognize that many such embodiments are possible, and the foregoing is not intended to limit the spirit, scope or intent of the invention.

Patient(s)202are representative of individuals at various stage in the life cycle. The life cycle includes birth, youth, teen, young adult, adult, middle age, gerontology and death. Patient(s)202are unique and have specific data and records. Patient(s) include both human and animals. Animals can be pets and livestock.

The patient devices204can refer to an information handling system100as described inFIG.1, and can include for example as a personal computer, a laptop computer, a tablet computer, a personal digital assistant (PDA), a smart phone, a mobile telephone, or other device that is capable of communicating and processing data. As commonly known, patient devices204can be considered as Internet of Things (IoT) that includes physical devices (or groups of devices) that can include or access sensors, with the processing capabilities and software to connect and exchange data with other devices and system over the network140including the Internet.

Various implementation provide for patient(s)202and patient device(s)204to connect to various sensor(s)206. Sensor(s)206can include patient wearable devices, such as patches, watches, etc. that are configured to monitor and gather patient202data. Such patient sensor(s)206are further described herein. Furthermore, sensor(s)206can include environmental sensors that monitor and gather environmental data experienced at patient device(s)204. Such environmental sensor(s)206can be included in patient device(s)204.

Implementations provide for the system200to include an e-commerce lifestyle store208, which can be a website(s) accessed through network140. As described above, information handling system100can include an e-commerce lifestyle application124. The e-commerce lifestyle application124can be used by patient(s)202to access the e-commerce lifestyle store208for goods and/or services.

Implementations provide for the system200to include an East Asian Medicine (EAM) service210, which can be a web site(s) accessed through network140. As described above, information handling system100can include an EAM application120. The EAM application120can be used by patient(s)202to access the EAM service210, for services such as diagnostics and recommendations related to health care based on EAM principles. The EAM service210is further described herein.

The system200can provide access to elements of system200, by health professional(s)212through health professional device(s)214. The health professional device(s)214access other elements of system200through network140. Implementations provide for the health professional device(s)214to be embodied as an information handling system100as described inFIG.1.

The system200can further include one or more websites or services as represented by website/service216-1to216-N. Examples of website/service216include real time weather or environmental websites or services, world clock websites, and other data sites. Examples of websites216include National Oceanic and Atmospheric Administration (NOAA) website, weather.com website, etc.

A principle of EAM is “Wu Yin Liu Qi,” which has five components of weather, earth, plants, animals, and human. The five components can be considered as Internet of Medical Things (IoMT). Weather/weather phenomena are studied and correlated to heath of patient(s)202. Changing weather, weather patterns as related to earth, space and relationship to health of patient(s)202is a principle of “Wu Yin Liu Qi.” “Wu Yin” can be interpreted as relating to change of time, direction, etc. of five seasons of spring, summer, late summer, fall and winter. “Liu Qi” can be interpreted as to six kids of climate change: wind, dampness, summer-heat, fire-heat, dryness, and cold.

Data from weather and climate related websites/services222correlate with location of patient(s)202, and particularly patient device(s)204. Such data can be really time data and can include time stamps indicating the time such data is collected/gathered. Implementations provide for the various elements of system200to access websites/services222and use the data for the methods described herein.

Implementations provide for the system200to include patient health records database(s)218, and other data database(s)220. The patient health records database218can be accessed and/or written to by elements of system200. In particular, patient device(s)204, health professional device(s)214, and EAM service210can access and/or write to patient health records database(s)218. Other data database(s)220can include data folders, files, etc. which include gathered and processed data by elements of system200as to patient(s)202.

FIG.3shows a block diagram of an example configuration and interaction300of a patient202with patient devices204and bioinformatic sensors, such as sensor(s)206. A patient202interacts with a patient device204. Implementations provide for patient device204to include the EAM app120and a user interface302. The user interface can be used by patient202to enter data and information. The user interface302can also provide for data and information to be provided to patient202, such as outcome, diagnostic, recommendation, cancer prediction, etc. data and information.

Implementations also provide for the patient devices204to include sensors304(e.g., environmental sensors206), which are configured to monitor environmental conditions, such as humidity, temperature, etc. which are used to derive outcomes in EAM. Implementations can include for the patient devices204to include communications such as NFC/Bluetooth306. In particular, such communications can be used to receive data from bioinformatic sensors (e.g., sensors206) of the patient202.

In certain implementations, left hand308and right hand310of patient202include a radial (on radial artery) patch312-1and a radial patch312-2. Implementations provide for either one or two patches312. Patch312can measure the radial pulse of patient202as well as temperature, skin moisture/humidity, sweat, oxygenation, etc. EAM relies on what is referred to as the four humors related to heat cold, dryness, and dampness. Patches312are configured to gather patient specific data as to the four humors. The data can be gathered in real time and time correlated. EAC relates organ rate variability or how organs of the patient202are affected with variability in the four humors.

Implementations provide for the patches312to operate in a time based series to track potential health risk patterns in real time using temperature and organ rate variability. Dynamic organ health variability is a time based series that is determinative of organs such as the heart, small intestine, bladder, kidney, pericardium, three warmers, gall bladder, liver, lungs, large intestine, stomach, and spleen. The left and right side of the patient202may be specific to particular organs. Therefore, two patches312may be implemented for particular organs; however, it is to be understood that one patch312may be implemented.

The patch312can include one or more sensors314. Each sensor314can be configured to perform a particular measurement, such temperature, oxygenation, pulse rate, dampness/dryness, etc. For example, the data from the sensors314can be received by patient devices204through NFC/Bluetooth306. Patches312are one implementation of a wearable device. Other wearable devices can be implemented to gather data as described herein. In various implementations, the use of such wearable devices is used with in medical interviews with health professional(s)212.

The human body is considered to have 12 systems consisting of the skeletal system, the nervous system, the muscular system, the respiratory system, the endocrine system, the immune system, the cardiovascular system, the circulatory system, the urinary system, the integumentary system, the reproductive system, and the digestive system. Implementations provide for wearable devices, such as patches312, to be configured to remotely monitor the 12 systems related to temperature, cold, heat, dampness, heart rate variability, and organ rate variability. Various implementations provide for data from wearable devices to be provided to a health engine as further described herein.

EAM principles include body maps and graph zones describing the condition of patient(s)202. Implementations provide for the wearable devices to provide data to a knowledge base server, provide dynamic health category of concern detected in real time on a dynamic body map, provide dynamic health score populated by customer changes (spirit, mind, body) lifestyle, and provided curated content based upon preferences.

Implementations provide for wearable devices to be used with herbal application122and e-commerce lifestyle store208. Wearable devices provide input/data, and particularly content source identity from curated content and entrance to with herbal application122and e-commerce lifestyle store208. Data from wearable devices can also be stored and found in other data database(s)220, particularly folders and files.

FIG.4shows example screen presentations of graphical user interfaces (GUI) for EAM data gathering, diagnostics, and patient recommendations. The user interface302can include GUI402and GUI404. As an example, GUI402can be used for entering information by patient(s)202and for receiving information through patient device(s)204. GUI404is an example user interface that can be used with weather health engine further describe herein, that predicts health based on dynamic weather conditions/changes.

FIG.5shows examples of EAM systems/principles500. EAM theory can include the following theory: four stages of Yin Yang (excess/deficiency); five phases that include wood, fire, metal and water; eight rubrics (diagnostics); ten stems (celestial phenomena changes); twelve branches (earth time stamp changes); twelve organs and channels; twenty four jie qi (predictable periodic earth changes); twenty eight constellations; sixty cycles and sixty four hexagrams.

In particular, the EAM systems/principles500are used to create and detect dynamic health changes. Examples of EAM systems/principles500can include the Wu Yun Liu Qi502, Qi Men Dun Jia (four mystic doors)504, and Bazi (four pillars or eight characters)506. It is to be understood that other EAM systems or principles can be implemented as part of EAM systems/principles500.

FIG.6shows components of a EAM service210. As described, implementations provide for patient devices204to connect with EAM service210. In particular, implementations provide for EAM application120to connect with an receive data and information from EAM service210. Embodiments include EAM service210can be provided by one or more computing devices, such as server computers. Embodiments can also provide that EAM service210to be implemented in a cloud computing environment.

Embodiments provide for the EAM service210to include EAM systems/principles500. The EAM systems/principles500can be implemented in one or more databases (e.g., other data database(s)220) as part of or accessible by the EAM service210. Implementations provide for the EAM service210to include an artificial intelligence/machine learning engine600. The artificial intelligence/machine learning engine600can be implemented to receive data and information as further described herein and refine and provide for more accurate input that is used by other engines and applications of the EAM service210.

Implementations provide for the EAM service210to include a weather health engine602. Weather health engine602is implemented to combine dynamic health changes according to weather patterns, such as received from data sources such as online websites/services216. Such health weather application receives results from OM service208and can integrate with the system checking engine214, patch312, and cancer predictor engine220. The weather health engine602detects adverse weather patterns that can affect patient(s)202health and provide predictions as to changes in dynamic health conditions. In addition, implementations provide for the Weather health engine602to provide curated content as predictive, preventative, and prescriptive recommendations.

In general, the weather health engine602determines effects (interior) to the patient(s)202. The weather health engine602identifies with sky, humans, animals, soil, botany/plants, insects. The weather health engine602provides data for EAM graph zones as to weather and temperature. EAM graph zones are provided data from website216data points (e.g., NOAA) translated for human, animals, plants and insects. A dynamic health score can be provided based on changes upon the hour, day, week, month, year, geolocation, lifestyle, etc. A dynamic health category can be flagged if there is a concern.

Implementations provide for weather health engine602to interact with herbal application122and e-commerce lifestyle store208. Weather health engine602can provide input/data, and particularly content source identity from curated content and entrance to with herbal application122and e-commerce lifestyle store208. Data from weather health engine602can also be stored and found in other data database(s)220, particularly folders and files.

Implementations provide for the EAM service210to include a health engine (outcome/diagnostics)604. In various implementations the health engine604provides an action to support health professional(s)212to conduct private medical interviews (PMI) with patient(s)202. Patient(s)202can be identified as to name, birth date, address, country, medical history, current medical history, medical disclaimer, various questions, etc.

The health engine604provides data for EAM graph zones as to an inquiry process: observation, listening, palpation, smelling, taste, etc. which can be provided to a knowledge based server. A dynamic health score can be provided. A dynamic health category can be flagged if there is a concern. A dynamic circular biomedicine risks assessment of the12systems can be performed as to EAM risk. Curated content can be performed based upon preferences and assessments.

Implementations provide for health engine604to interact with herbal application122and e-commerce lifestyle store208. Health engine604can provide input/data, and particularly content source identity from curated content. Data from health engine604can also be stored and found in other data database(s)220, particularly folders and files.

Implementations provide for the EAM service210to include a symptoms checking engine606, an outcome/diagnostic engine608, a recommendation engine610and cancer predictor engine612as further described herein. Implementations provide for the symptoms checking engine606to be a whole health system checker that predicts curated content for probable disease/health states based on different time intervals, such as hourly, daily, weekly, monthly and annually.

Implementations provide for the EAM service210to include and provide for a patient dynamic health body map612. The dynamic health body map612can provide real time changes. In various implementations, the dynamic health body map612is a multidimensional clickable thermographic (human) body graph that provides for surface to bone diagnosis of the12biomedical systems, and EAM meridian channel systems diagnosis. Furthermore, the patient(s)202are matched as to real time height, weight, etc., when health professional(s)212conduct private medical interviews (PMI) with patient(s)202. The dynamic health body map612can be share data with other systems and libraries and can change as to weather.

The dynamic health body map612provides data for EAM graph zones as to an inquiry process: observation, listening, palpation, smelling, taste, etc. which can be provided to a knowledge based server. Photographs or images of the tongue, eye and face can be mapped for each zone as to EAM. A dynamic health score can be provided. A dynamic health category can be flagged if there is a concern on the dynamic health body map612. A dynamic health score can be automatically populated by patient(s)202per questions answered. Curated content can be performed based upon references.

Implementations provide for dynamic health body map612to interact with herbal application122and e-commerce lifestyle store208. Dynamic health body map612can provide input/data, and particularly content source identity from curated content and entrance to with herbal application122and e-commerce lifestyle store208. Data from dynamic health body map612can also be stored and found in other data database(s)220, particularly folders and files.

FIG.7shows an interaction of components of EAM service210. In various embodiments the artificial intelligence/machine learning engine600includes one or more artificial neural networks700. Generally, neural networks700are used to solve or refine answers to artificial intelligence or machine learning problems, and specifically performing health related problems or issues related to EAM. The neural networks700can include an input layer, middle/hidden layer, and output layer. Furthermore, neural networks700can be one of various types of neural networks, such as recurrent neural networks, concurrent neural networks, etc.

Embodiments provide for the EAM service210and components to receive external inputs702, which include as described above, data and information for patient devices204, websites/services216, patient health records218, other data database(s)220and health professional devices214.

Embodiments provide for artificial intelligence/machine learning engine600to received external inputs, and input from EAM systems/principles500, symptoms checking engine606, weather health engine602, health engine (outcome/diagnostic)604, recommendation engine610, and cancer predictor engine608. Implementation can provide for neural networks700to receive the inputs in an input layer, process the inputs in a hidden layer and provide results in an output layer.

Implementations can include that the symptoms checking engine606to receive inputs from the artificial intelligence/machine learning engine210and EAM systems/principles500and provide for an output. As discussed, implementations can provide for the symptoms checking engine606to be a whole health system checker that predicts curated content for probable disease/health states based on different time intervals, such as hourly, daily, weekly, monthly and annually.

Implementations provide for the weather health engine602to receive inputs from the artificial intelligence/machine learning engine600and symptoms checking engine606and provide for an output.

Implementations provide for the health engine (outcome/diagnostic)604to receive inputs from the artificial intelligence/machine learning engine600and symptoms checking engine606and provide for an output.

Implementations can provide for recommendation engine610to receive inputs from the artificial intelligence/machine learning engine600, symptoms checking engine606, weather health engine602, and health engine (outcome/diagnostic)604, and provide for an output.

The cancer predictor engine608can receive inputs from artificial intelligence/machine learning engine600, symptoms checking engine606, weather health engine602, health engine (outcome/diagnostic)604, and recommendation engine610. The cancer predictor engine608relies on the dynamic changes to the health of a patient(s)202based on the principles of EAM. Therefore, the outputs of the respective components are determinative of cancer predictor engine608predictions.

FIG.8shows an example screen presentation of a prediction from the cancer predictor engine220.

FIG.9is a generalized flowchart900for gathering data, performing machine learning and providing patient analytics based on East Asian Medicine. In various embodiments, the system200is implemented. The order in which the method is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method, or alternate method. Additionally, individual blocks may be deleted from the method without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or a combination thereof, without departing from the scope of the invention.

At block902, process900starts. At step904, patient and environmental data related to the data are collected. The collection can be performed dynamically over a period of time (i.e., time based or time variant). At step906, using artificial intelligence/machine learning, and principles of EAM the collected data is processed. At step908, the collected data, principles of EAM and output from artificial intelligence/machine learning are processed through symptoms checking. At step910, health or disease status is provided based on the output processed by symptoms checking. Health or disease status can be based on different time intervals. At step912, diagnostics based on outputs from that artificial intelligence/machine learning and symptoms checking are provided. At step914, the process900ends.