An NLP-based cognitive system for disease status identification in electronic health records

Homa Alemzadeh, Murthy Devarakonda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper presents a natural language processing (NLP) based cognitive decision support system that automatically identifies the status of a disease from the clinical notes of a patient record. The system relies on IBM Watson Patient Record NLP analytics and supervised or semi-supervised learning techniques. It uses unstructured text in clinical notes, data from the structured part of a patient record, and disease control targets from the clinical guidelines. We evaluated the system using de-identified patient records of 414 hypertensive patients from a multi-specialty hospital system in the U.S. The experimental results show that, using supervised learning methods, our system can achieve an average 0.86 F1-score in identifying disease status passages and average accuracy of 0.77 in classifying the status as controlled or not. To the best of our knowledge, this is the first system to automatically identify disease control status from clinical notes.

Original languageEnglish (US)
Title of host publication2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages89-92
Number of pages4
ISBN (Electronic)9781509041794
DOIs
StatePublished - Apr 11 2017
Externally publishedYes
Event4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States
Duration: Feb 16 2017Feb 19 2017

Other

Other4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
CountryUnited States
CityOrlando
Period2/16/172/19/17

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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