AIMC Topic: Critical Illness

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The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods.

BMC infectious diseases Aug 4, 2025
BACKGROUND: Acute cholangitis (AC) presents with significant clinical heterogeneity, and existing severity classifications have limited prognostic value in critically ill patients. Subtypes of AC in critically ill patients have not been investigated.
Humans Prognosis Retrospective Studies Male Middle Aged Unsupervised Machine Learning Acute Disease Critical Illness Cholangitis Intensive Care Units Female Aged
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Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.

European journal of medical research Jul 24, 2025
BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in critically ill patients, marked by symmetrical respiratory and limb muscle weakness, which adversely affects long-term outcomes. Early identification of hi...
Machine Learning Algorithms Muscle Weakness Prospective Studies Prognosis Male Female Middle Aged Risk Factors Risk Assessment Humans Aged Critical Illness ROC Curve Intensive Care Units
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Machine learning for the prediction of augmented renal clearance (ARC) in patients with sepsis in critical care units.

Scientific reports Jul 18, 2025
This study aims to establish and validate prediction models based on novel machine learning (ML) algorithms for augmented renal clearance (ARC) in critically ill patients with sepsis. Patients with sepsis were extracted from the Medical Information M...
Male Middle Aged Sepsis Kidney Critical Illness Intensive Care Units Female Aged Creatinine Humans Machine Learning Algorithms
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AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

Science advances Jul 18, 2025
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcircu...
Prognosis Critical Illness Intensive Care Units Female Aged Humans Neural Networks, Computer Prospective Studies ROC Curve Skin Hyperspectral Imaging Male Middle Aged Deep Learning Sepsis
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Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator.

PloS one Jul 16, 2025
BACKGROUND: Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often...
Aged Male Middle Aged Risk Assessment Sepsis Delirium Critical Illness Intensive Care Units Female Risk Factors Humans Machine Learning Algorithms Retrospective Studies
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.

BMC medical informatics and decision making Jul 15, 2025
BACKGROUND: Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorr...
Critical Illness Intensive Care Units Female Aged Humans Risk Assessment Machine Learning Hospital Mortality Prognosis Male Hemorrhage Retrospective Studies Middle Aged
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Bias in vital signs? Machine learning models can learn patients' race or ethnicity from the values of vital signs alone.

BMJ health & care informatics Jul 10, 2025
OBJECTIVES: To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone.
Intensive Care Units Female Aged Adult United States Bias Ethnicity Male Middle Aged Machine Learning Algorithms Retrospective Studies Critical Illness Heart Rate Vital Signs Racial Groups Humans Respiratory Rate
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Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.

BMC medical informatics and decision making Jul 4, 2025
BACKGROUND: The incidence of intensive care unit (ICU) admissions and the corresponding mortality rates among cancer patients are both high. However, the existing scoring systems all lack specificity. This research seeks to establish and validate a p...
Male Middle Aged Neoplasms Hospital Mortality Nomograms Critical Illness Humans Intensive Care Units Machine Learning Female Retrospective Studies Aged
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Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.

BMC medical informatics and decision making Jul 3, 2025
BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient co...
Male Middle Aged Malnutrition Critical Illness Humans Female Intensive Care Units Machine Learning Aged Prospective Studies Adult
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Development and validation of a modified SOFA score for mortality prediction in candidemia patients.

Scientific reports Jul 1, 2025
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...
Middle Aged Risk Assessment Organ Dysfunction Scores Kaplan-Meier Estimate Candidemia Humans Intensive Care Units Female Machine Learning Aged Prognosis Male Critical Illness
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