AIMC Topic: Delirium

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Mapping the Delirium Literature Through Probabilistic Topic Modeling and Network Analysis: A Computational Scoping Review.

Psychosomatics Dec 22, 2018
BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically-ill populations. Delirium is recognized, through both professional competencies and instructional materials, as a core topic in consultat...
Delirium Humans Probability Psychiatry Bibliometrics
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Applying machine learning to continuously monitored physiological data.

Journal of clinical monitoring and computing Nov 11, 2018
The use of machine learning (ML) in healthcare has enormous potential for improving disease detection, clinical decision support, and workflow efficiencies. In this commentary, we review published and potential applications for the use of ML for moni...
Decision Support Systems, Clinical Monitoring, Physiologic Randomized Controlled Trials as Topic Adult Respiration, Artificial Sepsis Critical Care Expert Systems Wavelet Analysis Clinical Alarms Delirium Arrhythmias, Cardiac Atrial Fibrillation Machine Learning Software Algorithms Databases, Factual Intensive Care Units Electronic Health Records Humans
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Development and Validation of an Electronic Health Record-Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment.

JAMA network open Aug 3, 2018
IMPORTANCE: Current methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy.
Male Machine Learning Humans Middle Aged Forecasting Delirium Retrospective Studies Risk Assessment Aged Models, Educational Adolescent Electronic Health Records Young Adult Cognitive Dysfunction Female Adult Cohort Studies Hospitalization
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Machine Learning for Prediction in Electronic Health Data.

JAMA network open Aug 3, 2018
Electronic Health Records Cognitive Dysfunction Delirium Humans Machine Learning
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Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

Physiological measurement Mar 27, 2018
OBJECTIVE: Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distingu...
Heart Rate Early Diagnosis Middle Aged Electrocardiography Delirium Humans Machine Learning Female Male Prognosis Intensive Care Units
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GC-MS analysis of the designer drug α-pyrrolidinovalerophenone and its metabolites in urine and blood in an acute poisoning case.

Forensic science international Dec 20, 2015
α-Pyrrolidinovalerophenone (α-PVP) is a synthetic cathinone belonging to the group of "second generation" pyrrolidinophenones that becomes more and more popular as a designer psychostimulant. Here we provide toxicological analytical support for a sev...
Acute Disease Delirium Gas Chromatography-Mass Spectrometry Pyrrolidines Designer Drugs Humans Adult Male
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POETenceph - Automatic identification of clinical notes indicating encephalopathy using a realist ontology.

AMIA ... Annual Symposium proceedings. AMIA Symposium Nov 5, 2015
Identifying inpatients with encephalopathy is important. The disorder is prevalent, often missed, and puts patients at risk. We describe POETenceph, natural language processing pipeline, which ranks clinical notes on the extent to which they indicate...
Humans Algorithms Medical Records Information Storage and Retrieval Biological Ontologies Brain Diseases Inpatients Natural Language Processing Delirium
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Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study.

International journal of medical informatics Jul 1, 2025
OBJECTIVE: To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients.
Female Male Aged Logistic Models Aged, 80 and over Humans Emergence Delirium Delirium Machine Learning Prospective Studies
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Delirium Identification from Nursing Reports Using Large Language Models.

Studies in health technology and informatics May 15, 2025
This study investigates large language models for delirium detection from nursing reports, comparing keyword matching, prompting, and finetuning. Using a manually labelled dataset from the University Hospital Freiburg, Germany, we tested Llama3 and P...
Delirium Data Mining Germany Electronic Health Records Sensitivity and Specificity Reproducibility of Results Diagnosis, Computer-Assisted Large Language Models Humans Natural Language Processing Nursing Records Nursing Diagnosis
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Participatory Design of an AI-Based CDSS for Delirium Prevention.

Studies in health technology and informatics May 15, 2025
Delirium is a frequent and severe complication in inpatient care, leading to increased mortality and cognitive impairment. The KIDELIR project aims to develop a clinical decision support system (CDSS) based on artificial intelligence (AI) to predict ...
Humans Artificial Intelligence Decision Support Systems, Clinical Delirium
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