A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
In the last few years, the trend in health care of embracing artificial intelligence (AI) has
dramatically changed the medical landscape. Medical centres have adopted AI applications …
dramatically changed the medical landscape. Medical centres have adopted AI applications …
Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review
Background Artificial intelligence (AI) and machine learning (ML) models continue to evolve
the clinical decision support systems (CDSS). However, challenges arise when it comes to …
the clinical decision support systems (CDSS). However, challenges arise when it comes 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 …
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 …
Time-to-event modeling for hospital length of stay prediction for COVID-19 patients
Providing timely patient care while maintaining optimal resource utilization is one of the
central operational challenges hospitals have been facing throughout the pandemic …
central operational challenges hospitals have been facing throughout the pandemic …
A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals
ME Samadi, J Guzman-Maldonado, K Nikulina… - Scientific reports, 2024 - nature.com
The development of reliable mortality risk stratification models is an active research area in
computational healthcare. Mortality risk stratification provides a standard to assist physicians …
computational healthcare. Mortality risk stratification provides a standard to assist physicians …
M3T-LM: A multi-modal multi-task learning model for jointly predicting patient length of stay and mortality
Ensuring accurate predictions of inpatient length of stay (LoS) and mortality rates is essential
for enhancing hospital service efficiency, particularly in light of the constraints posed by …
for enhancing hospital service efficiency, particularly in light of the constraints posed by …
HSGA: A Hybrid LSTM-CNN Self-Guided Attention to predict the future diagnosis from discharge narratives
The prognosis of a patient's re-admission and the forecast of future diagnoses is a critical
task in the process of inferring clinical outcomes. The discharge summaries recorded in the …
task in the process of inferring clinical outcomes. The discharge summaries recorded in the …
Prediction of intensive care unit length of stay in the MIMIC-IV dataset
Accurately estimating the length of stay (LOS) of patients admitted to the intensive care unit
(ICU) in relation to their health status helps healthcare management allocate appropriate …
(ICU) in relation to their health status helps healthcare management allocate appropriate …
Predicting the stay length of patients in hospitals using convolutional gated recurrent deep learning model
Predicting hospital length of stay (LoS) stands as a critical factor in shaping public health
strategies. This data serves as a cornerstone for governments to discern trends, patterns …
strategies. This data serves as a cornerstone for governments to discern trends, patterns …