Observational studyBMC health services research2020
Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm.
Observational study in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Simulation-Based Education to Support Nurses' Critical Thinking in Delirium Recognition: A Scoping Review.Journal of advanced nursing · 2026Article
- Direct and Indirect Statistical Associations Between Knowledge, Attitudes, and Behaviours Regarding Subsyndromal Delirium in Psychiatric Nurses: A Cross-Sectional Study.Nursing open · 2026Article
- Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes.PLOS digital health · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Article
- Analysis of risk factors for subjective stress among nurses caring for patients with delirium: a cross-sectional, multicentre survey.BMC nursing · 2025Article
- Delirium pathophysiology in cancer: neurofilament light chain biomarker - narrative review.BMJ supportive & palliative care · 2025Review
- Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study.Journal of medical Internet research · 2024Article
- Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.International journal of clinical pharmacy · 2024Article
- Delirium in psychiatric settings: risk factors and assessment tools in patients with psychiatric illness: a scoping review.BMC nursing · 2024Article
- Identification and information management of cognitive impairment of patients in acute care hospitals: An integrative review.International journal of nursing sciences · 2024Review
- Altered Tryptophan-Kynurenine Pathway in Delirium: A Review of the Current Literature.International journal of molecular sciences · 2023Review
- A lung cancer risk warning model based on tongue images.Frontiers in physiology · 2023Article
- Boosting Delirium Identification Accuracy With Sentiment-Based Natural Language Processing: Mixed Methods Study.JMIR medical informatics · 2022Article
- Social determinants of health and the prediction of missed breast imaging appointments.BMC health services research · 2022Article
- Liaison psychiatry before and after the COVID-19 pandemic.Psychiatry research · 2022Article
- Positive scores on the 4AT delirium assessment tool at hospital admission are linked to mortality, length of stay and home time: two-centre study of 82,770 emergency admissions.Age and ageing · 2022Article
- Psychiatry in the Digital Age: A Blessing or a Curse?International journal of environmental research and public health · 2021Review
- Drug Treatment for Patients with Postoperative Delirium and Consultation-Liaison Psychiatry in Japan: A Retrospective Observational Study of a Nationwide Hospital Claims Database.Annals of clinical epidemiology · 2021Article
- Cognitive Computing-Based CDSS in Medical Practice.Health data science · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDelirium 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.
objectivesTo 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.
methodA 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.
resultsThirty 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.
conclusionsDelirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.
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