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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study.

Lu Wang^1,2, Yilun Zhang¹, Mark Chignell¹

¹Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.

JMIR Medical Informatics

|December 20, 2022

Summary

This summary is machine-generated.

Machine learning models improved delirium identification in hospitalized patients using natural language processing (NLP). Sentiment analysis of medical reports significantly enhanced model accuracy, aiding in delirium diagnosis and prevention research.

Keywords:

data mining delirium diagnosis medical image description sentiment analysis text mining and analysis

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Neurocognitive Disorders

Background:

Delirium is a common acute neurocognitive disorder in older hospitalized patients, associated with adverse outcomes like dementia and increased mortality.
Accurate identification and prediction of delirium are challenging, hindering effective prevention and treatment strategies.
Up to 50% of older medical inpatients experience delirium, underscoring the need for improved diagnostic tools.

Purpose of the Study:

To enhance machine learning (ML) models for retrospective delirium identification during hospital stays.
To evaluate the impact of natural language processing (NLP) techniques, specifically sentiment analysis, on ML model performance for delirium detection.
To assess the utility of NLP in analyzing diagnostic imaging reports for delirium-related sentiment.

Main Methods:

A dataset of nearly 4000 hospital admissions from 6 Toronto hospitals was manually reviewed.
Machine learning models were developed and compared, with and without the integration of NLP applied to diagnostic imaging reports.
Sentiment analysis was employed as an NLP feature to identify sentiment towards or away from a delirium diagnosis.

Main Results:

Out of 3862 eligible admissions, 994 (25.74%) were identified as having delirium.
The ML model incorporating NLP achieved an accuracy of 0.807 and an AUC of 0.930.
The ML model without NLP achieved an accuracy of 0.811 and an AUC of 0.869, indicating NLP's significant contribution.

Conclusions:

Machine learning models incorporating NLP, specifically sentiment analysis of medical image descriptions, provide valid and significantly improved identification of delirium.
Sentiment analysis in text mining of diagnostic imaging reports offers substantial benefits for ML-based delirium detection.
The developed ML model demonstrated stable performance over a 5-year period, suggesting its robustness for future applications.