Generating A ccurate E lectronic H ealth A ssessment from M edical G raph (2020)

Abstract: One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients’ prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians’ evaluation showed that MCAG could generate high-quality assessments.

## introduction electronic health record (ehr) is widely used by hospitals in the united states and other countries, resulting in an unprecedented amount of digital data or ehrs associated with patient encounters. in recent years, secondary use of ehrs has helped advance ehr-related computational approaches to foster precision medicine and a learning health system @xcite . rich clinical information is documented in the ehrs. among many structures and formats in ehrs, a problem-oriented soap (subjective, objective, assessment, and plan) structure is commonly used by providers @xcite . figure 1 illustrate an example of a soap note for an outpatient encounter. typically, chief complaint includes a brief description of a patient's conditions and the reasons for the visit. the subjec-tive section is a detailed report of the patient's current conditions, such as source, onset, and duration of symptoms, mainly based on the patient's selfreport. this section usually includes a history of present illness and symptoms, current medications, and allergies. the objective section documents the results of physical exam findings, laboratory data, vital signs, and descriptions of imaging results. the assessment section typically contains medical diagnoses and reasons that lead to medical diagnoses. the assessment is typically based on the content from the chief complaint, and the subjective and objective sections. the plan section addresses treatment plans based on the assessment. inferring clinical diagnosis to generate an assessment is a crucial step during the patient encounter. earlier expert systems were mainly knowledgebased, typically using decision rules. later, machine learning approaches were developed, mainly used longitudinal electronic health records (ehr) to predict icd codes @xcite , the diagnostic codes assigned to ehrs after each patient's visit or encounter. however, icd codes are used mainly for billing purposes and have limitations (e.g., incomplete assignment) when used as the gold standard diagnoses labels @xcite . in this study, we propose an alternative task. instead of predicting icd codes, we intend to build an expert system by directly generating medical assessments. we accomplish the task of automated assessment text generation using supervised machine-learning. specifically, our system's input is the free-text of chief complaint, subjective sections, and objective sections. the output is the assessment. we train our supervised machine learning models based on the soap-structured ehr notes as a text to text generation nlp application. the challenges of this text to text generation include: a strong baseline model for automated assessment generation is a pointer-generator model n2mag @xcite . although the model helps mitigate the out-of-vocabulary challenge, it however does not address the challenge of limited word overlap between the subjective and objective sections and the assessment. therefore, we propose a new model for automated clinical assessment generation (mcag), which generates assessment using knowledge graph. specifically, we treat our assessment generation as a concept-to-text generation problem. we first build a local or patient-specific concept graph by nlp-processing the free text of the subjective and objective sections. we then expand this patient-specific concept graph with background knowledge extracted from an external and comprehensive knowledge resource, the unified medical language system (umls) @xcite . once we build the concept-graph, we train the mcag model end-to-end. our mcag mitigates both challenges mentioned above. first, concept normalization (for example, "mi", "myocardial infarction" and "heart attack" can be mapped to the same concept) helps mitigate the out-of-vocabulary word (e.g., mi) challenge. the patient-specific concept graph helps generate the reasons for the diagnosis, and the expanded concept graph with the background knowledge helps infer novel text (diagnosis) not described in the input text (i.e., chief complaint, subjective and objective sections). the contributions of our work are threefold: ## conclusion in this paper, we propose a novel task of generating medical assessment from not only patient specific medical information but also relevant backgrounds. we adapt the graph transformer model to our task and meanwhile proposed an additional approach to address the lack of relevant background medical knowledge. experiments show that graph transformer outperforms text pointer-generator model, even without the help of additional background medical knowledge. in addition, enhancing the graph with relevant medical knowledge could further improve the generated assessment quality. experiments also show the current text-to-text transformer pretrained on large corpus may learn limit medical domain-specific knowledge. further generation quality improvements could be made by incorporating domain-specific knowledge graphs. in the future, we plan to explore: (1) probing tasks to randomly switch some entities to other irrelevant and improper tokens, and see if graph model is more resilient to these noises; (2) many ehrs are follow-up ehrs that is based on the previous ehr. we wish to further expand ehrs in time step by applying temporal graph models to incorporate temporal information.