UMMS Affiliation

Department of Medicine

Publication Date

2020-01-15

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Diagnosis | Health Information Technology | Health Services Administration | Health Services Research | Theory and Algorithms

Abstract

BACKGROUND: Since 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.

OBJECTIVE: We aimed to report a natural language processing system for generating medical assessments based on patient information described in the electronic health record (EHR) notes.

METHODS: We 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.

RESULTS: We 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.

CONCLUSIONS: N2MAG 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.

Keywords

artificial intelligence, deep neural network model, electronic health record note, medical assessment generation, natural language processing

Rights and Permissions

© Baotian Hu, Adarsha Bajracharya, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org),15.01.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

DOI of Published Version

10.2196/14971

Source

Hu B, Bajracharya A, Yu H. Generating Medical Assessments Using a Neural Network Model: Algorithm Development and Validation. JMIR Med Inform. 2020 Jan 15;8(1):e14971. doi: 10.2196/14971. PMID: 31939742; PMCID: PMC7006435. Link to article on publisher's site

Journal/Book/Conference Title

JMIR medical informatics

Related Resources

Link to Article in PubMed

PubMed ID

31939742

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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