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[HTML][HTML] Explainable, trustworthy, and ethical machine learning for healthcare: A survey
With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …
for critical applications like healthcare, the questions about liability, trust, and interpretability …
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 …
Graphcare: Enhancing healthcare predictions with personalized knowledge graphs
Clinical predictive models often rely on patients' electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …
integrating medical knowledge to enhance predictions and decision-making is challenging …
Multi-modal learning for inpatient length of stay prediction
Predicting inpatient length of stay (LoS) is important for hospitals aiming to improve service
efficiency and enhance management capabilities. Patient medical records are strongly …
efficiency and enhance management capabilities. Patient medical records are strongly …
Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis
S Gokhale, D Taylor, J Gill, Y Hu, N Zeps… - Frontiers in …, 2023 - frontiersin.org
Background Unwarranted extended length of stay (LOS) increases the risk of hospital-
acquired complications, morbidity, and all-cause mortality and needs to be recognized and …
acquired complications, morbidity, and all-cause mortality and needs to be recognized and …
Risk Stratification Index 3.0, a broad set of models for predicting adverse events during and after hospital admission
S Greenwald, GF Chamoun, NG Chamoun… - …, 2022 - ingentaconnect.com
Background: Risk stratification helps guide appropriate clinical care. Our goal was to
develop and validate a broad suite of predictive tools based on International Classification of …
develop and validate a broad suite of predictive tools based on International Classification of …
Prediction of mortality risk and duration of hospitalization of COVID-19 patients with chronic comorbidities based on machine learning algorithms
Background The severity of coronavirus (COVID-19) in patients with chronic comorbidities is
much higher than in other patients, which can lead to their death. Machine learning (ML) …
much higher than in other patients, which can lead to their death. Machine learning (ML) …
Predicting next-day discharge via electronic health record access logs
Objective Hospital capacity management depends on accurate real-time estimates of
hospital-wide discharges. Estimation by a clinician requires an excessively large amount of …
hospital-wide discharges. Estimation by a clinician requires an excessively large amount of …
[HTML][HTML] Machine learning–based hospital discharge prediction for patients with cardiovascular diseases: development and usability study
Background: Effective resource management in hospitals can improve the quality of medical
services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and …
services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and …
[HTML][HTML] Optimizing discharge after major surgery using an artificial intelligence–based decision support tool (DESIRE): An external validation study
Background In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we
have previously developed and validated a machine learning concept in 1,677 …
have previously developed and validated a machine learning concept in 1,677 …