AIMC Topic: Patient Discharge

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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open Aug 10, 2019
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.
Sweden Aged Aged, 80 and over Young Adult Machine Learning Retrospective Studies Electronic Health Records Female Logistic Models Emergency Service, Hospital Registries Patient Discharge Mortality Adult Male Middle Aged Time Factors Humans
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Readmission prediction using deep learning on electronic health records.

Journal of biomedical informatics Jul 24, 2019
Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted inter...
Humans Neural Networks, Computer Electronic Health Records Computational Biology Models, Statistical Aged, 80 and over Female Patient Discharge Male Sweden Heart Failure Deep Learning Risk Factors Patient Readmission Time Factors Aged Expert Systems Cost Savings
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Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

International journal of medical informatics May 25, 2019
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to ...
Humans Machine Learning Algorithms Electronic Health Records Adverse Drug Reaction Reporting Systems Medical Errors Natural Language Processing Singapore Patient Discharge Drug-Related Side Effects and Adverse Reactions
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Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua.

Medical care research and review : MCRR Apr 29, 2019
Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the perform...
Nicaragua Humans Child Emergency Service, Hospital Machine Learning Patient Discharge Databases, Factual
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Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

BioMed research international Apr 15, 2019
BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive ...
Predictive Value of Tests Male Patient Discharge Child Models, Biological Humans Patient Readmission Machine Learning Female Risk Factors ROC Curve Support Vector Machine
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Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society Apr 2, 2019
PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place aft...
Quality Improvement Patient Transfer Rehabilitation Centers Skilled Nursing Facilities Middle Aged Predictive Value of Tests Postoperative Care Elective Surgical Procedures Lumbar Vertebrae Patient Discharge Spinal Stenosis Netherlands Algorithms Female Aged Male Humans Neural Networks, Computer Machine Learning
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Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society Mar 27, 2019
PURPOSE: We aimed to develop a machine learning algorithm that can accurately predict discharge placement in patients undergoing elective surgery for degenerative spondylolisthesis.
Female Aged Male Middle Aged Models, Statistical Patient Discharge Spondylolisthesis Humans Machine Learning Algorithms Databases, Factual
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Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

BMJ open Mar 7, 2019
OBJECTIVE: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.
Humans Patient Discharge Critical Care Patient Readmission Female Male England Machine Learning Length of Stay Algorithms Decision Support Techniques Electronic Health Records
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Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

JAMA network open Nov 2, 2018
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.
Models, Statistical Patient Discharge Humans Academic Medical Centers Machine Learning New England Forecasting Hospitals
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An improved support vector machine-based diabetic readmission prediction.

Computer methods and programs in biomedicine Oct 12, 2018
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...
Decision Trees Bayes Theorem Patient Discharge Patient Readmission Risk Assessment Decision Support Systems, Clinical Logistic Models Support Vector Machine Humans Neural Networks, Computer Algorithms Reproducibility of Results Female Risk Factors Sensitivity and Specificity Male
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