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

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Published on: January 11, 2020

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm.

Catherine Hercus¹, Abdul-Rahman Hudaib²

¹Alfred Hospital, Melbourne, Australia.

BMC Health Services Research

|February 29, 2020

Summary

Delirium is frequently misdiagnosed in hospitals. A machine learning model can accurately predict delirium misdiagnosis, improving patient care and diagnostic accuracy.

Keywords:

Delirium Input variables Machine learning-logistic classifier Misdiagnosis

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Area of Science:

Psychiatry
Medical Informatics
Data Science

Background:

Delirium is a common diagnosis in Consultation-Liaison Psychiatry (CLP).
Studies indicate frequent misdiagnosis of delirium before CLP referral.
Multivariate analyses of delirium misdiagnosis factors are limited.

Purpose of the Study:

Quantify delirium misdiagnosis rates upon CLP referral.
Develop a predictive classifier for delirium misdiagnosis.
Identify key variables influencing delirium misdiagnosis.

Main Methods:

Retrospective observational study at Alfred Hospital.
Data collected over 5 months on factors contributing to misdiagnosis.
Machine Learning-Logistic Regression model developed to classify delirium diagnosis accuracy.

Main Results:

35 out of 74 new cases (47%) were misdiagnosed.
Predictive algorithm achieved 79% ROC AUC.
Classification accuracy was 72%, with 77% sensitivity and 67% specificity.

Conclusions:

Delirium is often misdiagnosed in hospital environments.
Machine learning-logistic predictive classifiers show promise for healthcare applications.
Improved diagnostic accuracy for delirium is achievable with predictive modeling.