AIMC Topic: Patient Admission
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Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.
International journal of mental health nursing
Aug 29, 2024
This research addresses the critical issue of identifying factors contributing to admissions to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T...
From admission to discharge: a systematic review of clinical natural language processing along the patient journey.
BMC medical informatics and decision making
Aug 29, 2024
BACKGROUND: Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice...
Improving ED admissions forecasting by using generative AI: An approach based on DGAN.
Computer methods and programs in biomedicine
Aug 8, 2024
BACKGROUND AND OBJECTIVE: Generative Deep Learning has emerged in recent years as a significant player in the Artificial Intelligence field. Synthesizing new data while maintaining the features of reality has revolutionized the field of Deep Learning...
Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jul 30, 2024
OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and fu...
Early Detection of Pulmonary Embolism in a General Patient Population Immediately Upon Hospital Admission Using Machine Learning to Identify New, Unidentified Risk Factors: Model Development Study.
Journal of medical Internet research
Jul 30, 2024
BACKGROUND: Under- or late identification of pulmonary embolism (PE)-a thrombosis of 1 or more pulmonary arteries that seriously threatens patients' lives-is a major challenge confronting modern medicine.
An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease.
Respiratory research
Jun 18, 2024
BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-ba...
Interpretable machine learning predicts postpartum hemorrhage at time of admission.
American journal of obstetrics & gynecology MFM
May 12, 2024
Early heart rate variability evaluation enables to predict ICU patients' outcome.
Scientific reports
Feb 15, 2022
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such var...
Respiration, Artificial
Severity of Illness Index
Length of Stay
Hospital Mortality
Electrocardiography
Humans
Oxygen Saturation
Neural Networks, Computer
Heart Rate
Machine Learning
Respiratory Function Tests
Female
Aged
Patient Admission
Prospective Studies
Male
Prognosis
Middle Aged
Intensive Care Units
Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.
PloS one
Jan 24, 2022
Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship betw...
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.
Scientific reports
Nov 26, 2021
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historic...
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