AIMC Topic: Delirium

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Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

Age and ageing May 1, 2024
INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop...
Aged, 80 and over Male Risk Assessment Female Risk Factors Aged Predictive Value of Tests Delirium Geriatric Assessment Postoperative Complications Humans Support Vector Machine Age Factors Machine Learning Algorithms
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Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data.

Studies in health technology and informatics Jun 6, 2022
Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the lab...
Machine Learning Support Vector Machine Delirium Humans
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Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.

The journals of gerontology. Series A, Biological sciences and medical sciences Mar 3, 2022
BACKGROUND: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natura...
Humans Algorithms Electronic Health Records Aged Natural Language Processing Delirium International Classification of Diseases
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Physiological Assessment of Delirium Severity: The Electroencephalographic Confusion Assessment Method Severity Score (E-CAM-S).

Critical care medicine Jan 1, 2022
OBJECTIVES: Delirium is a common and frequently underdiagnosed complication in acutely hospitalized patients, and its severity is associated with worse clinical outcomes. We propose a physiologically based method to quantify delirium severity as a to...
Image Processing, Computer-Assisted Electroencephalography Patient Acuity Length of Stay Hospital Mortality Delirium Academic Medical Centers Confusion Adult Male Middle Aged Hospitals Comorbidity Severity of Illness Index Retrospective Studies Humans Female Aged Machine Learning Prognosis
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Letter to the editor in response to "Risk prediction of delirium in hospitalized patients using machine learning: an implementation and prospective evaluation study".

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
Humans Delirium Machine Learning Prospective Studies Patients
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Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to iden...
Female Hospitalization Aged Aged, 80 and over Male Middle Aged Algorithms Prospective Studies Models, Theoretical Electronic Health Records Humans Risk Assessment Workflow Machine Learning ROC Curve Delirium
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Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients.

Studies in health technology and informatics Jun 23, 2020
Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Observation Screening Scale (DOSS) or the Confusion Assessment Method (CAM). The aim of this work is t...
Machine Learning Delirium Humans International Classification of Diseases
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An Improvised Classification Model for Predicting Delirium.

Studies in health technology and informatics Aug 21, 2019
With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, th...
Humans Machine Learning Logistic Models Delirium
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Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?

Studies in health technology and informatics Jan 1, 2019
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms ...
Hospitals Algorithms Delirium Humans Prognosis Machine Learning Electronic Health Records
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Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Studies in health technology and informatics Jan 1, 2019
BACKGROUND: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.
Data Accuracy Workflow Delirium Humans Machine Learning Databases, Factual Electronic Health Records
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