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Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7045404/ https://www.ncbi.nlm.nih.gov/pubmed/32106845 http://dx.doi.org/10.1186/s12913-020-5005-1 |
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author | Hercus, Catherine Hudaib, Abdul-Rahman |
author_facet | Hercus, Catherine Hudaib, Abdul-Rahman |
author_sort | Hercus, Catherine |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7045404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70454042020-03-03 Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm Hercus, Catherine Hudaib, Abdul-Rahman BMC Health Serv Res Research Article 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. BioMed Central 2020-02-27 /pmc/articles/PMC7045404/ /pubmed/32106845 http://dx.doi.org/10.1186/s12913-020-5005-1 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Hercus, Catherine Hudaib, Abdul-Rahman Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title | Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title_full | Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title_fullStr | Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title_full_unstemmed | Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title_short | Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
title_sort | delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7045404/ https://www.ncbi.nlm.nih.gov/pubmed/32106845 http://dx.doi.org/10.1186/s12913-020-5005-1 |
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