MIE : A Medical Information Extractor towards Medical Dialogues (2020)

Abstract: Electronic Medical Records (EMRs) have become key components of modern medical care systems. Despite the merits of EMRs, many doctors suffer from writing them, which is time-consuming and tedious. We believe that automatically converting medical dialogues to EMRs can greatly reduce the burdens of doctors, and extracting information from medical dialogues is an essential step. To this end, we annotate online medical consultation dialogues in a window-sliding style, which is much easier than the sequential labeling annotation. We then propose a Medical Information Extractor (MIE) towards medical dialogues. MIE is able to extract mentioned symptoms, surgeries, tests, other information and their corresponding status. To tackle the particular challenges of the task, MIE uses a deep matching architecture, taking dialogue turn-interaction into account. The experimental results demonstrate MIE is a promising solution to extract medical information from doctor-patient dialogues.

## introduction with the advancement of the informatization process of the medical system, electronic medical records (emrs) are required by an increasing number of hospitals all around the world. compared with conventional medical records, emrs are easy to save and retrieve, which bring considerable convenience for both patients and doctors. furthermore, emrs allow medical researchers to investigate the implicit contents included, such as epidemiologic study and patient cohorts finding. ## related work extracting information from medical texts is a longterm objective for both biomedical and nlp community. for example, the 2010 i2b2 challenge provides a popular dataset still used in many recent researches @xcite . three tasks were presented: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. extracting medical information from dialogues just gets started. @xcite proposed a pipeline method to generate emrs. the approach contains five steps: dialogue role labeling, automatic speech recognition (asr), knowledge extraction, structured data processing and natural language generation (nlg) @xcite . the most important part is knowledge extraction, which uses dictionary, regular expression and other supervised machine learning methods. however, the detailed explanations are left out, which make us hard to compare with them. @xcite aimed at generating emrs by extracting symptoms and their status. they defined 186 symptoms and three status, i.e., experienced, not experienced and other. they proposed two models to tackle the problem. span-attribute tagging model first predicted the span of a symptom, and then used the context features to further predict the symptom name and status. the seq2seq model took k dialogue turns as input, and then directly generated the symptom name and status. they collected incredible 90k dialogues and annotated 3k of them, but the dataset is not public. the most similar work to ours is @xcite , which also annotated chinese online medical dialogues. concretely, they annotated 2,067 dialogues with the bio (begin-in-out) schema. there are two main components, namely symptom recognition and symptom inference in their approach. the former utilized both document-level and corpus-level attention enhanced conditional random field (crf) to acquire symptoms. the letter serves determining the symptom status. our work differs from @xcite and @xcite mainly in the following two points: a) we only extract 45 symptom items, but the status are more detailed, furthermore, we extract surgeries, tests and other information; b) we use different extracting method. since the annotation system is different, our approach does not need the sequential labeling, which relieves the labeling work. ## our approach in this section, we will elaborate the proposed mie model, a novel deep matching neural network model. deep matching models are widely used in multiple natural language processing tasks such as machine reading comprehension @xcite , question answering @xcite and dialogue generation @xcite . compared with classification models, matching models are able to introduce more information of the candidate side and promote interaction between both ends. the architecture of mie is shown in figure 2 . there are four main components, namely encoder module, matching module, aggregate module and scorer module. the input of mie is a doctor-patient i can't breathe out. it seems that there is phlegm in my throat. has cardiac ultrasound been done? no, what medicine should i take for myocarditis? do you have breathing difficulties and diagnosed myocarditis now? i have difficulty in breathing occasionally. scorer module category item status symptom chest pain doctor-pos test ultrasonic patient-neg surgery pci doctor-pos ... ... ... category encoder status encoder candidate encoder matching module candidate scores ... medical dialogue candidates 𝐻 1 𝐻 1 𝐻 2 𝐻 2 ... 𝐻 𝑛 𝐻 𝑛 𝐶 𝑛 𝐶 𝑛 category encoder status encoder utterance encoder aggregate module 𝑞 1 ... 𝑞 1 𝑞 2 𝑞 2 𝑞 𝑛 𝑞 𝑛 𝑓 1 𝑓 2 ... 𝑓 𝑚 y dialogue window, and the output is the predicted medical information. ## experiments in this section, we will conduct experiments on the proposed dataset. it is worth to note that we are not going to compare mie with @xcite and @xcite , because a) they all employed sequential labeling methods, leading to different evaluation dimensions from ours (theirs are more strict as they must give the exact symptom positions in the original utterance), and b) their approaches were customized for sequential labeling paradigm, thus cannot be re-implemented in our dataset. ## conclusion and future work in this paper, we first describe a new constructed corpus for the medical information extraction task, including the annotation methods and the evaluation metrics. then we propose mie, a deep neural matching model tailored for the task. mie is able to capture the interaction information between the dialogue turns. to show the advantage of mie, we develop several competitive baselines for comparison. the experimental results indicate that mie is a promising solution for medical information extraction towards medical dialogues. in the future, we should further leverage the internal relations in the candidate end, and try to introduce rich medical background knowledge into our work.