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Observational Study
. 2020 Feb 27;20(1):151.
doi: 10.1186/s12913-020-5005-1.

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

Catherine Hercus  1 , Abdul-Rahman Hudaib  2
Affiliations
Observational Study

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

Catherine Hercus et al. BMC Health Serv Res. .

Abstract

Background: Delirium is a frequent diagnosis made by Consultation-Liaison Psychiatry (CLP). Numerous studies have demonstrated misdiagnosis prior to referral to CLP. Few studies have considered the factors underlying misdiagnosis using multivariate approaches.

Objectives: To determine the number of cases referred to CLP, which are misdiagnosed at time of referral, to build an accurate predictive classifier algorithm, using input variables related to delirium misdiagnosis.

Method: A retrospective observational study was conducted at Alfred Hospital in Melbourne, collecting data from a record of all patients seen by CLP for a period of 5 months. Data was collected pertaining to putative factors underlying misdiagnosis. A Machine Learning-Logistic Regression classifier model was built, to classify cases of accurate delirium diagnosis vs. misdiagnosis.

Results: Thirty five of 74 new cases referred were misdiagnosed. The proposed predictive algorithm achieved a mean Receiver Operating Characteristic (ROC) Area under the curve (AUC) of 79%, an average 72% classification accuracy, 77% sensitivity and 67% specificity.

Conclusions: Delirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.

Keywords: Delirium; Input variables; Machine learning-logistic classifier; Misdiagnosis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
ROC curve representing the diagnostic accuracy of the proposed predictive classifier. As diagnostic test accuracy improves, the ROC curve moves toward the left and the AUC (Area under the curve) approaches 1
See this image and copyright information in PMC

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