AIMC Topic: Hospitalization
Clear Filters Showing 471 to 480 of 506 articles
Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections.
The American journal of managed care
Oct 1, 2020
OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare beneficiaries at risk of severe respiratory infections in community and postacute settings by (1) identifying individuals in a community setting at risk of in...
A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.
The journal of trauma and acute care surgery
Oct 1, 2020
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...
Hospitalization
Humans
Young Adult
Neural Networks, Computer
Proportional Hazards Models
Machine Learning
Adult
Multivariate Analysis
Prognosis
Male
Injury Severity Score
Middle Aged
Organ Dysfunction Scores
Wounds and Injuries
Retrospective Studies
Sepsis
Critical Illness
ROC Curve
Intensive Care Units
Sodium
Female
Critical Care
Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.
Cardiovascular drugs and therapy
Aug 1, 2020
PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascular events in patients with myocardial infarction (MI). Using the pooled clinical data from patients with MI and GI in the completed ABC and PPAR trials...
Hospitalization
Aged
Male
Middle Aged
Blood Glucose
Prognosis
Retrospective Studies
Female
Risk Factors
Heart Failure
Data Mining
Natriuretic Peptide, Brain
Proton Pump Inhibitors
Angina, Stable
Randomized Controlled Trials as Topic
Percutaneous Coronary Intervention
Coronary Artery Disease
Myocardial Infarction
Humans
Artificial Intelligence
Databases, Factual
Biomarkers
Recurrence
Glucose Intolerance
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...
Neural Networks for Cause of Hospitalization and Final Cause of Death Extraction from Discharge Summaries.
Studies in health technology and informatics
Jun 16, 2020
Determining the cause of death of hospitalized patients with cardiovascular disease is of the utmost importance. This is usually recorded in free text form. In this study we aimed to develop a series of supervised natural language processing algorith...
Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.
Anesthesia and analgesia
May 1, 2020
BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated...
Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.
Anesthesiology
May 1, 2020
BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of disch...
Commentary: Laser Ablation of Abnormal Neurological Tissue Using Robotic Neuroblate System (LAANTERN): Procedural Safety and Hospitalization.
Neurosurgery
Apr 1, 2020
Temporal convolutional networks allow early prediction of events in critical care.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2020
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.
Using applied machine learning to predict healthcare utilization based on socioeconomic determinants of care.
The American journal of managed care
Jan 1, 2020
OBJECTIVES: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data.
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