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

Baotian Hu1, Adarsha Bajracharya2, Hong Yu1,3,4

  • 1Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, United States.

JMIR Medical Informatics
|January 16, 2020
PubMed
Summary

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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This summary is machine-generated.

This study introduces N2MAG, a novel AI system that generates medical assessments from electronic health record notes. N2MAG accurately infers clinical diagnoses from patient symptoms and physician observations, outperforming existing methods.

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Natural Language Processing

Background:

  • Clinical diagnosis is a critical aspect of patient care, historically relying on expert systems or machine learning for ICD code prediction.
  • Existing methods often use electronic health records (EHRs) to predict diagnoses, but an alternative approach focuses on direct inference from clinical observations and patient-reported symptoms.

Purpose of the Study:

  • To develop and report a natural language processing (NLP) system for generating medical assessments.
  • To leverage patient information within EHR notes for automated medical assessment generation.

Main Methods:

  • EHR notes were processed into Subjective, Objective, Assessment, and Plan (SOAP) sections.
  • A neural network model for medical assessment generation (N2MAG) was trained using an end-to-end approach.
Keywords:
artificial intelligencedeep neural network modelelectronic health record notemedical assessment generationnatural language processing

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  • N2MAG utilizes the Subjective and Objective sections of EHR notes, requiring no feature engineering or external knowledge resources.

    • Main Results:

    • N2MAG demonstrated superior performance compared to baseline models in generating medical assessments.
    • Quantitative evaluation using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics and qualitative assessment by domain experts confirmed N2MAG's effectiveness.
    • The model successfully generated "expert-like" assessments, indicating its potential in clinical settings.

    Conclusions:

    • The N2MAG system can effectively generate medical assessments by analyzing the Subjective and Objective sections of EHR notes.
    • Future research will explore N2MAG's utility in providing clinical decision support to healthcare professionals.