A systematic literature review of predicting patient discharges using statistical methods and machine learning
Discharge planning is integral to patient flow as delays can lead to hospital-wide
congestion. Because a structured discharge plan can reduce hospital length of stay while …
congestion. Because a structured discharge plan can reduce hospital length of stay while …
Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining …
OBJECTIVE Modern surgical planning and prognostication requires the most accurate
outcomes data to practice evidence-based medicine. For clinicians treating children …
outcomes data to practice evidence-based medicine. For clinicians treating children …
Prediction of emergency department hospital admission based on natural language processing and neural networks
Objective: To describe and compare logistic regression and neural network modeling
strategies to predict hospital admission or transfer following initial presentation to …
strategies to predict hospital admission or transfer following initial presentation to …
Length of hospital stay prediction at the admission stage for cardiology patients using artificial neural network
PF Tsai, PC Chen, YY Chen, HY Song… - Journal of healthcare …, 2016 - Wiley Online Library
For hospitals' admission management, the ability to predict length of stay (LOS) as early as
in the preadmission stage might be helpful to monitor the quality of inpatient care. This study …
in the preadmission stage might be helpful to monitor the quality of inpatient care. This study …
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging
OBJECTIVE Prognostication and surgical planning for WHO grade I versus grade II
meningioma requires thoughtful decision-making based on radiographic evidence, among …
meningioma requires thoughtful decision-making based on radiographic evidence, among …
[HTML][HTML] A statistical method for predicting quantitative variables in association rule mining
S Mohammed, K Rubarth, SK Piper, F Schiefenhövel… - Information Systems, 2023 - Elsevier
Background: Association rules encode common patterns and structures identified in
datasets. They can be derived by association rule mining (ARM) algorithms. The association …
datasets. They can be derived by association rule mining (ARM) algorithms. The association …
Using an artificial neural network to predict traumatic brain injury
OBJECTIVE Pediatric traumatic brain injury (TBI) is common, but not all injuries require
hospitalization. A computational tool for ruling in patients who will have a clinically relevant …
hospitalization. A computational tool for ruling in patients who will have a clinically relevant …
Machine-learning prediction for hospital length of stay using a French medico-administrative database
Abstract Introduction: Prolonged Hospital Length of Stay (PLOS) is an indicator of
deteriorated efficiency in Quality of Care. One goal of public health management is to reduce …
deteriorated efficiency in Quality of Care. One goal of public health management is to reduce …
Data analytics for the sustainable use of resources in hospitals: predicting the length of stay for patients with chronic diseases
Various factors are behind the forces that drive hospitals toward more sustainable
operations. Hospitals contracting with Medicare, for instance, are reimbursed for the …
operations. Hospitals contracting with Medicare, for instance, are reimbursed for the …
Neural network prediction of ICU length of stay following cardiac surgery based on pre-incision variables
RJ LaFaro, S Pothula, KP Kubal, ME Inchiosa… - PLoS …, 2015 - journals.plos.org
Background Advanced predictive analytical techniques are being increasingly applied to
clinical risk assessment. This study compared a neural network model to several other …
clinical risk assessment. This study compared a neural network model to several other …