Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation
Extensive monitoring in intensive care units (ICUs) generates large quantities of data which
contain numerous trends that are difficult for clinicians to systematically evaluate. Current …
contain numerous trends that are difficult for clinicians to systematically evaluate. Current …
Machine learning in patient flow: a review
This work is a review of the ways in which machine learning has been used in order to plan,
improve or aid the problem of moving patients through healthcare services. We decompose …
improve or aid the problem of moving patients through healthcare services. We decompose …
Artificial intelligence in critical care
The modern father of artificial intelligence (AI), Professor Andrew Ng (Stanford University),
once described AI as the “new electricity.” Over the last few years, research and innovation …
once described AI as the “new electricity.” Over the last few years, research and innovation …
Prediction of ICU readmissions using data at patient discharge
Unplanned readmissions to ICU contribute to high health care costs and poor patient
outcomes. 6-7% of all ICU cases see a readmission within 72 hours. Machine learning …
outcomes. 6-7% of all ICU cases see a readmission within 72 hours. Machine learning …
External validation and comparison of a general ward deterioration index between diversely different health systems
BC Cummings, JM Blackmer, JR Motyka… - Critical Care …, 2023 - journals.lww.com
OBJECTIVES: Implementing a predictive analytic model in a new clinical environment is
fraught with challenges. Dataset shifts such as differences in clinical practice, new data …
fraught with challenges. Dataset shifts such as differences in clinical practice, new data …
A clinical decision support system for edge/cloud ICU readmission model based on particle swarm optimization, ensemble machine learning, and explainable artificial …
ICU readmission is usually associated with an increased number of hospital death.
Predicting readmission helps to reduce such risks by avoiding early discharge, providing …
Predicting readmission helps to reduce such risks by avoiding early discharge, providing …
A locally optimized data-driven tool to predict sepsis-associated vasopressor use in the ICU
OBJECTIVES: To train a model to predict vasopressor use in ICU patients with sepsis and
optimize external performance across hospital systems using domain adaptation, a transfer …
optimize external performance across hospital systems using domain adaptation, a transfer …
A machine learning solution for bed occupancy issue for smart healthcare sector
S Gochhait, SA Butt, E De-La-Hoz-Franco… - Automatic Control and …, 2021 - Springer
The health care domain is a culmination and emergence of many other economic sectors
that give different services from patient treatment to healing, protective, rehabilitation, and …
that give different services from patient treatment to healing, protective, rehabilitation, and …
Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines
S Hegselmann, C Ertmer, T Volkert, A Gottschalk… - Frontiers in …, 2022 - frontiersin.org
Background Intensive care unit (ICU) readmissions are associated with mortality and poor
outcomes. To improve discharge decisions, machine learning (ML) could help to identify …
outcomes. To improve discharge decisions, machine learning (ML) could help to identify …
Big data analysis y machine learning en medicina intensiva
AN Reiz, MAA de la Hoz, MS García - Medicina Intensiva, 2019 - Elsevier
Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine
Learning (ML), due to the huge amount of information processed and stored in electronic …
Learning (ML), due to the huge amount of information processed and stored in electronic …