[HTML][HTML] Opening the black box: the promise and limitations of explainable machine learning in cardiology
Many clinicians remain wary of machine learning because of longstanding concerns about
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
“black box” models.“Black box” is shorthand for models that are sufficiently complex that they …
Artificial intelligence and machine learning in precision and genomic medicine
S Quazi - Medical Oncology, 2022 - Springer
The advancement of precision medicine in medical care has led behind the conventional
symptom-driven treatment process by allowing early risk prediction of disease through …
symptom-driven treatment process by allowing early risk prediction of disease through …
[HTML][HTML] Current applications of big data and machine learning in cardiology
R Cuocolo, T Perillo, E De Rosa, L Ugga… - Journal of geriatric …, 2019 - ncbi.nlm.nih.gov
Abstract Machine learning (ML) is a software solution with the ability of making predictions
without prior explicit programming, aiding in the analysis of large amounts of data. These …
without prior explicit programming, aiding in the analysis of large amounts of data. These …
Machine learning in the prediction of medical inpatient length of stay
Length of stay (LOS) estimates are important for patients, doctors and hospital
administrators. However, making accurate estimates of LOS can be difficult for medical …
administrators. However, making accurate estimates of LOS can be difficult for medical …
[HTML][HTML] Hospital patients' length of stay prediction: A federated learning approach
Predicting patient's length of stay (LOS) is a crucial determinant for hospitals to maintain
resource efficiency and quality treatment, where machine learning-based predictive …
resource efficiency and quality treatment, where machine learning-based predictive …
Radiomic machine learning classifiers in spine bone tumors: a multi-software, multi-scanner study
V Chianca, R Cuocolo, S Gitto, D Albano, I Merli… - European Journal of …, 2021 - Elsevier
Purpose Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics
and machine learning (ML) have proven useful even in absence of a standardized data …
and machine learning (ML) have proven useful even in absence of a standardized data …
Forecasting emergency department overcrowding: A deep learning framework
As the demand for medical cares has considerably expanded, the issue of managing patient
flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to …
flow in hospitals and especially in emergency departments (EDs) is certainly a key issue to …
[HTML][HTML] Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients
Robust and rabid mortality prediction is crucial in intensive care units because it is
considered one of the critical steps for treating patients with serious conditions. Combining …
considered one of the critical steps for treating patients with serious conditions. Combining …
Predicting the length-of-stay of pediatric patients using machine learning algorithms
N Boff Medeiros, FS Fogliatto… - … Journal of Production …, 2025 - Taylor & Francis
The management of hospitals' resource capacity has a strong impact on the quality of care,
and the length-of-stay (LOS) of patients is an indicator that reflects its efficiency and …
and the length-of-stay (LOS) of patients is an indicator that reflects its efficiency and …
Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework
Recently, the hospital systems face a high influx of patients generated by several events,
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …
such as seasonal flows or health crises related to epidemics (eg, COVID'19). Despite the …