University of Tasmania
Browse
Using Natural Language Processing to Predict Risk in Electronic Health Records.pdf (290.12 kB)

Using Natural Language Processing to Predict Risk in Electronic Health Records

Download (290.12 kB)
Version 2 2024-02-04, 23:39
Version 1 2023-09-11, 00:26
conference contribution
posted on 2024-02-04, 23:39 authored by Duy Van Le, Erin MontgomeryErin Montgomery, Kenneth Kirkby, Joel ScanlanJoel Scanlan

Clinical narratives recording behaviours and emotions of patients are available from EHRs in a forensic psychiatric centre located in Tasmania. This rich data has not been used in risk prediction. Prior work demonstrates natural language processing can be used to identify patient symptoms in these free-text records and can then be used to predict risk. Four dictionaries containing descriptive words of harm were created using the Diagnostic and Statistical Manual of Mental Disorders, the Unified Medical Language System repository, English negative and positive sentiment words, and high-frequency words from the Corpus of Contemporary American English. However, a model based only on these keywords is limited in predictive power. In this study, we introduce an improved NLP approach with a social interaction component to extract additional information about the behavioural and emotional state of patients. These social interactions are subsequently used in a machine-learning model to enhance risk prediction performance.

History

Volume

310

Pagination

574-578

Department/School

Australian Institute of Health Service Management (AIHSM), Information and Communication Technology

Publisher

IOS Press

Publication status

  • Published

Event title

MedInfo 2023

Event Venue

International Convention Centre Sydney (ICC), Sydney, Australia

Date of Event (Start Date)

2023-07-08

Date of Event (End Date)

2023-07-12

Rights statement

© 2024 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/SHTI231030

UN Sustainable Development Goals

3 Good Health and Well Being

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC