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Patient Discharge

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Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

JAMA network open
IMPORTANCE: Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve patient safety at periods of elevated risk.

An improved support vector machine-based diabetic readmission prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In healthcare systems, the cost of unplanned readmission accounts for a large proportion of total hospital payment. Hospital-specific readmission rate becomes a critical issue around the world. Quantification and early ident...

Predicting hospital admission at emergency department triage using machine learning.

PloS one
OBJECTIVE: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions.

Applied clinical informatics
BACKGROUND: Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs.

Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation.

International journal of medical informatics
OBJECTIVES: Prediction of activities of daily living (ADL) is crucial for optimized care of post-stroke patients. However, no suitably-validated and practical models are currently available in clinical practice.

Mild troponin elevation in patients admitted to the emergency department with atrial fibrillation: 30-day post-discharge prognostic significance.

Internal and emergency medicine
Patients with atrial fibrillation (AF) often undergo troponin (Tn) testing in the emergency department (ED), but the clinical significance of mildly elevated values remains unclear. We evaluated short-term 30-day post-discharge outcomes in AF patient...

Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks.

BMC medical research methodology
BACKGROUND: Outcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical ...