AIMC Topic: Intensive Care Units
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Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.
Clinical interventions in aging
Jun 12, 2024
BACKGROUND: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develo...
Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.
PeerJ
Jun 12, 2024
BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the ...
Machine Learning-Aided Decision-Making Model for the Discontinuation of Continuous Renal Replacement Therapy.
Blood purification
Jun 12, 2024
INTRODUCTION: Continuous renal replacement therapy (CRRT) is a primary form of renal support for patients with acute kidney injury in an intensive care unit. Making an accurate decision of discontinuation is crucial for the prognosis of patients. Pre...
Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.
Tomography (Ann Arbor, Mich.)
Jun 7, 2024
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether t...
A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission.
BMC medical informatics and decision making
Jun 7, 2024
BACKGROUND: This study aimed to develop a higher performance nomogram based on explainable machine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive ca...
Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.
Scientific reports
Jun 7, 2024
Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enr...
Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.
IEEE journal of biomedical and health informatics
Jun 6, 2024
In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph convolutional neural network. Our method addresses two critical challenges: the need for timely and...
Towards the automatic calculation of the EQUAL Candida Score: Extraction of CVC-related information from EMRs of critically ill patients with candidemia in Intensive Care Units.
Journal of biomedical informatics
Jun 5, 2024
OBJECTIVES: Candidemia is the most frequent invasive fungal disease and the fourth most frequent bloodstream infection in hospitalized patients. Its optimal management is crucial for improving patients' survival. The quality of candidemia management ...
LDSG-Net: an efficient lightweight convolutional neural network for acute hypotensive episode prediction during ICU hospitalization.
Physiological measurement
Jun 5, 2024
. Acute hypotension episode (AHE) is one of the most critical complications in intensive care unit (ICU). A timely and precise AHE prediction system can provide clinicians with sufficient time to respond with proper therapeutic measures, playing a cr...
Development and external validation of a machine learning model for the prediction of persistent acute kidney injury stage 3 in multi-centric, multi-national intensive care cohorts.
Critical care (London, England)
Jun 4, 2024
BACKGROUND: The aim of this retrospective cohort study was to develop and validate on multiple international datasets a real-time machine learning model able to accurately predict persistent acute kidney injury (AKI) in the intensive care unit (ICU).
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