AIMC Topic: Length of Stay

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Emergency Department Capacity Planning: A Recurrent Neural Network and Simulation Approach.

Computational and mathematical methods in medicine Nov 15, 2019
Emergency departments (EDs) play a vital role in the whole healthcare system as they are the first point of care in hospitals for urgent and critically ill patients. Therefore, effective management of hospital's ED is crucial in improving the quality...
Climate Hospital Administration Emergency Treatment Bed Occupancy Reproducibility of Results Humans Length of Stay Time Factors Neural Networks, Computer Emergency Service, Hospital Machine Learning Turkey Algorithms Computer Simulation
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ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Retrospective Studies Intensive Care Units Brazil Length of Stay Nurses Humans Physicians Odds Ratio Organ Dysfunction Scores Personnel Staffing and Scheduling Physical Therapists Time Factors Hospital Bed Capacity Hospital Mortality Logistic Models Cluster Analysis Unsupervised Machine Learning
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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records.

Journal of biomedical informatics Sep 24, 2019
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little acc...
Middle Aged Length of Stay Cluster Analysis Humans Machine Learning Algorithms Critical Illness Electronic Health Records Female Male
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Construct validation of machine learning in the prediction of short-term postoperative complications following total shoulder arthroplasty.

Journal of shoulder and elbow surgery Aug 3, 2019
BACKGROUND: We aimed to demonstrate that supervised machine learning (ML) models can better predict postoperative complications after total shoulder arthroplasty (TSA) than comorbidity indices.
Male Middle Aged Postoperative Complications Comorbidity Length of Stay Humans Forecasting Databases, Factual Female Aged Reoperation Predictive Value of Tests Serum Albumin Supervised Machine Learning ROC Curve Arthroplasty, Replacement, Shoulder Hematocrit Body Mass Index Patient Readmission Age Factors Smoking Blood Transfusion Operative Time
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Waterjet Ablation Therapy for Treating Benign Prostatic Obstruction in Patients with Small- to Medium-size Glands: 12-month Results of the First French Aquablation Clinical Registry.

European urology Jul 5, 2019
BACKGROUND: Aquablation has emerged as a novel ablative therapy combining image guidance and robotics for targeted waterjet adenoma resection.
Robotics Registries France Lower Urinary Tract Symptoms Ablation Techniques Length of Stay Treatment Outcome Operative Time Recovery of Function Prostatic Hyperplasia Humans Aged Male Middle Aged Postoperative Complications
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Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

The Journal of arthroplasty Jun 20, 2019
BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition before primary total knee arthroplasty (TKA). The secondary objective ...
Arthroplasty, Replacement, Knee Risk Factors Osteoarthritis, Knee Aged Aged, 80 and over Male Comorbidity Deep Learning Length of Stay ROC Curve United States Inpatients Neural Networks, Computer Humans Algorithms Databases, Factual Female Reproducibility of Results
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Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

The Journal of arthroplasty Jun 17, 2019
BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine.
Telemedicine Remote Consultation Risk Arthroplasty Orthopedics Length of Stay Big Data Monitoring, Physiologic Registries Analgesics, Opioid Humans Artificial Intelligence Machine Learning
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Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

The Journal of arthroplasty May 2, 2019
BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create...
Male Comorbidity Deep Learning Length of Stay Cohort Studies ROC Curve United States Inpatients Arthroplasty, Replacement, Hip Preoperative Period Databases, Factual Female Reproducibility of Results Aged Aged, 80 and over Social Class Health Care Costs Osteoarthritis, Hip Fees and Charges Humans
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Improving Outcomes Defending Patient Safety: The Learning Journey in Robotic Liver Resections.

BioMed research international Apr 8, 2019
BACKGROUND: While laparoscopy is currently adopted for hepatic resections, robotic approaches to the liver have not gained wide acceptance. We decided to analyze the learning curve in the field of robotic liver surgery comparing short-term outcomes b...
Liver Operative Time Robotics Humans Laparoscopy Robotic Surgical Procedures Patient Safety Middle Aged Retrospective Studies Postoperative Complications Female Length of Stay Male Liver Neoplasms Surgeons Blood Loss, Surgical Hepatectomy Learning Curve
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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.
Length of Stay Patient Discharge Critical Care Patient Readmission Decision Support Techniques Electronic Health Records Female Male England Humans Machine Learning Algorithms
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