Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation

Baotian Hu,Hong Yu,Adarsha Bajracharya

doi:10.2196/14971

Abstract

BackgroundSince its inception, artificial intelligence has aimed to use computers to help make clinical diagnoses. Evidence-based medical reasoning is important for patient care. Inferring clinical diagnoses is a crucial step during the patient encounter. Previous works mainly used expert systems or machine learning–based methods to predict the International Classification of Diseases - Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients’ reported symptoms and physicians’ clinical observations.ObjectiveWe aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes.MethodsWe processed EHR notes into the Subjective, Objective, Assessment, and Plan sections. We trained a neural network model for medical assessment generation (N2MAG). Our N2MAG is an innovative deep neural model that uses the Subjective and Objective sections of an EHR note to automatically generate an “expert-like” assessment of the patient. N2MAG can be trained in an end-to-end fashion and does not require feature engineering and external knowledge resources.ResultsWe evaluated N2MAG and the baseline models both quantitatively and qualitatively. Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models.ConclusionsN2MAG could generate a medical assessment from the Subject and Objective section descriptions in EHR notes. Future work will assess its potential for providing clinical decision support.

Highlights

Evaluated by both the Recall-Oriented Understudy for Gisting Evaluation metrics and domain experts, our results show that N2MAG outperformed the existing state-of-the-art baseline models
Electronic health record (EHR) systems have been widely adopted by hospitals in the United States and other countries [1], resulting in an unprecedented amount of digital data or EHRs associated with patient encounters [2]
The results show that both N2MAG and PG with the copying mechanism outperformed the Seq2Seq+att model

Summary

Introduction

Electronic health record (EHR) systems have been widely adopted by hospitals in the United States and other countries [1], resulting in an unprecedented amount of digital data or EHRs associated with patient encounters [2]. The primary function of EHRs is to document patients’ clinical information and share them among health care providers for patient care. Rich clinical information is represented in the EHRs. In recent years, secondary use of EHRs has helped advance EHR-related computational approaches [3,4]. EHR notes are written by providers who care for their patients. Previous works mainly used expert systems or machine learning–based methods to predict the International Classification of Diseases Clinical Modification codes based on electronic health records. We report an alternative approach: inference of clinical diagnoses from patients’ reported symptoms and physicians’ clinical observations

Methods

Results

Conclusion