An explainable machine learning framework for lung cancer hospital length of stay prediction
This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients
using machine learning (ML) models. The framework proposed to deal with imbalanced …
using machine learning (ML) models. The framework proposed to deal with imbalanced …
Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: development and multi-centric, multi-national external …
Background Acute Kidney Injury (AKI) is a major complication in patients admitted to
Intensive Care Units (ICU), causing both clinical and economic burden on the healthcare …
Intensive Care Units (ICU), causing both clinical and economic burden on the healthcare …
Metacare++: Meta-learning with hierarchical subtyping for cold-start diagnosis prediction in healthcare data
Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a
few visits per patient and a few observations per disease can be exploited. Although meta …
few visits per patient and a few observations per disease can be exploited. Although meta …
Temporal convolutional networks and data rebalancing for clinical length of stay and mortality prediction
It is critical for hospitals to accurately predict patient length of stay (LOS) and mortality in real-
time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to …
time. We evaluate temporal convolutional networks (TCNs) and data rebalancing methods to …
Predicting patient outcomes with graph representation learning
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU) has focused
heavily on the physiological time series data, largely ignoring sparse data such as …
heavily on the physiological time series data, largely ignoring sparse data such as …
A deep learning approach for inpatient length of stay and mortality prediction
Purpose Accurate prediction of the Length of Stay (LoS) and mortality in the Intensive Care
Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real …
Unit (ICU) is crucial for effective hospital management, and it can assist clinicians for real …
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 …
Predicting prolonged length of ICU stay through machine learning
This study aimed to construct machine learning (ML) models for predicting prolonged length
of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter …
of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter …
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 …
Multimodal fusion network for ICU patient outcome prediction
C Wang, X Yang, M Sun, Y Gu, J Niu, W Zhang - Neural Networks, 2024 - Elsevier
Over the past decades, massive Electronic Health Records (EHRs) have been accumulated
in Intensive Care Unit (ICU) and many other healthcare scenarios. The rich and …
in Intensive Care Unit (ICU) and many other healthcare scenarios. The rich and …