AIMC Topic: Patient Discharge
Clear Filters Showing 91 to 100 of 170 articles
Stroke prognostication for discharge planning with machine learning: A derivation study.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Aug 5, 2020
Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data t...
The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.
Journal of shoulder and elbow surgery
Jun 9, 2020
HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (...
Arthroplasty, Replacement, Shoulder
Hemiarthroplasty
Shoulder Injuries
Patient Discharge
Osteoarthritis
Hospital Costs
Hospital Charges
Reproducibility of Results
Aged
Aged, 80 and over
Male
Middle Aged
Postoperative Complications
Length of Stay
ROC Curve
Neural Networks, Computer
Models, Statistical
Machine Learning
Humans
Forecasting
Databases, Factual
Female
Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.
International journal of medical informatics
Apr 23, 2020
BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We ai...
A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model.
Medical & biological engineering & computing
Apr 23, 2020
The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission m...
Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach.
International journal of medical informatics
Apr 18, 2020
OBJECTIVE: Mental or substance use disorders (M/SUD) are major contributors of disease burden with high risk for hospital readmissions. We sought to develop and evaluate a readmission model using a machine learning (ML) approach.
Outpatient Robot-Assisted Radical Prostatectomy: Are Patients Ready for Same-Day Discharge?
Journal of endourology
Mar 26, 2020
Several case series have demonstrated the safety and feasibility of outpatient robot-assisted radical prostatectomy (RARP) in well-selected patients; however, the patient perspective of this practice has not been well explored. In this study, we exp...
Same-day discharge surgery for robot-assisted radical prostatectomy in the era of ERAS and prehabilitation pathways: a contemporary, comparative, feasibility study.
World journal of urology
Feb 17, 2020
PURPOSE: To assess the feasibility of same-day discharge (SDD) after robot-assisted radical prostatectomy (RARP) in the context of enhanced recovery after surgery (ERAS) and prehabilitation pathways.
Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.
JAMA network open
Dec 2, 2019
IMPORTANCE: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and r...
Mapping anatomical related entities to human body parts based on wikipedia in discharge summaries.
BMC bioinformatics
Aug 17, 2019
*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building m...
A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems.
Journal of healthcare engineering
Aug 15, 2019
Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry. However, nearly all existing systems are restricted to speci...
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