AIMC Topic: Patient Discharge
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Predicting Discharge Disposition Following Meningioma Resection Using a Multi-Institutional Natural Language Processing Model.
Neurosurgery
Mar 15, 2021
BACKGROUND: Machine learning (ML)-based predictive models are increasingly common in neurosurgery, but typically require large databases of discrete variables for training. Natural language processing (NLP) can extract meaningful data from unstructur...
Patient satisfaction with a pharmacist-led best possible medication discharge plan via tele-robot in a remote and rural community hospital.
Canadian journal of rural medicine : the official journal of the Society of Rural Physicians of Canada = Journal canadien de la medecine rurale : le journal officiel de la Societe de medecine rurale du Canada
Jan 1, 2021
INTRODUCTION: Medication reconciliation (MedRec) reduces the risk of preventable medication-related adverse events (ADEs). A best possible medication discharge plan (BPMDP) is a revised list of medications a patient will take when discharged from hos...
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...
Machine Learning With Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort.
Neurosurgery
Sep 15, 2020
BACKGROUND: Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk presents opportunities for actionable change to reduce readmission rates.
Machine Learning for Predictive Modeling of 90-day Readmission, Major Medical Complication, and Discharge to a Facility in Patients Undergoing Long Segment Posterior Lumbar Spine Fusion.
Spine
Aug 15, 2020
STUDY DESIGN: Retrospective case control study.
Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2020
With the development of advanced robotic hands, a reliable neural-machine interface is essential to take full advantage of the functional dexterity of the robots. In this preliminary study, we developed a novel method to estimate isometric forces of ...
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...
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...
Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk ...
Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2018
OBJECTIVE: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.
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