AIMC Topic: Intensive Care Units
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Interpretable machine learning model for predicting acute kidney injury in critically ill patients.
BMC medical informatics and decision making
May 31, 2024
BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques.
A deep learning approach for generating intracranial pressure waveforms from extracranial signals routinely measured in the intensive care unit.
Computers in biology and medicine
May 29, 2024
Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized tha...
Advances in the Application of AI Robots in Critical Care: Scoping Review.
Journal of medical Internet research
May 27, 2024
BACKGROUND: In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented...
Interpretable machine learning models for predicting the incidence of antibiotic- associated diarrhea in elderly ICU patients.
BMC geriatrics
May 24, 2024
BACKGROUND: Antibiotic-associated diarrhea (AAD) can prolong hospitalization, increase medical costs, and even lead to higher mortality rates. Therefore, it is essential to predict the incidence of AAD in elderly intensive care unit(ICU) patients. Th...
Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate.
Artificial intelligence in medicine
May 23, 2024
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships. In particular, we model each subject as a distribution over "topics...
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis.
Health care management science
May 21, 2024
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient's complete health history to make informed decisions about f...
Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.
BMC medical informatics and decision making
May 14, 2024
BACKGROUND: Predicting whether Carbapenem-Resistant Gram-Negative Bacterial (CRGNB) cause bloodstream infection when giving advice may guide the use of antibiotics because it takes 2-5 days conventionally to return the results from doctor's order.
Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.
Critical care (London, England)
May 10, 2024
BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous ...
Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP.
Intensive & critical care nursing
May 2, 2024
BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds.
AKIML: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.
Clinica chimica acta; international journal of clinical chemistry
May 1, 2024
BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in c...
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