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Organ Dysfunction Scores

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Assessment of Machine Learning to Estimate the Individual Treatment Effect of Corticosteroids in Septic Shock.

JAMA network open 33301017
IMPORTANCE: The survival benefit of corticosteroids in septic shock remains uncertain.
Aged Clinical Decision-Making Critical Care Female Fludrocortisone Glucocorticoids Humans Hydrocortisone Intensive Care Units Machine Learning Male Organ Dysfunction Scores Prognosis Randomized Controlled Trials as Topic Risk Adjustment Shock, Septic
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A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.

Critical care medicine 33711001
OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated ...
Adult Clinical Deterioration Critical Care Deep Learning Humans Male Middle Aged Organ Dysfunction Scores Pennsylvania Retrospective Studies Risk Assessment Sepsis
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Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

Frontiers in immunology 36325351
BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, ...
Critical Illness Humans Machine Learning Organ Dysfunction Scores Risk Factors ROC Curve
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Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Renal failure 35166177
PURPOSE: Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with ...
Acute Kidney Injury Aged Cerebrovascular Disorders China Critical Care Databases, Factual Female Humans Logistic Models Machine Learning Male Middle Aged Organ Dysfunction Scores Retrospective Studies ROC Curve
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Explainable Deep Learning Model for Predicting Serious Adverse Events in Hospitalized Geriatric Patients Within 72 Hours.

Clinical interventions in aging 38883992
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...
Aged Aged, 80 and over Area Under Curve Deep Learning Emergency Service, Hospital Female Geriatric Assessment Hospitalization Humans Intensive Care Units Male Organ Dysfunction Scores Prognosis Retrospective Studies Risk Assessment ROC Curve Taiwan
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Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

European journal of clinical investigation 38376066
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...
Aged Algorithms Bilirubin Causality Critical Illness Female Gastrointestinal Hemorrhage Glasgow Coma Scale Hospital Mortality Humans Intensive Care Units Length of Stay Machine Learning Male Middle Aged Organ Dysfunction Scores Prognosis
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Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

Cell reports. Medicine 39127039
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingl...
Aged Algorithms Anti-Bacterial Agents beta-Lactams Critical Illness Drug Monitoring Female Humans Machine Learning Male Middle Aged Organ Dysfunction Scores Sepsis
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Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

Biometrics 40037600
Heterogeneous treatment effect estimation is an important problem in precision medicine. Specific interests lie in identifying the differential effect of different treatments based on some external covariates. We propose a novel non-parametric treatm...
Algorithms Bayes Theorem Biometry Computer Simulation Humans Intensive Care Units Length of Stay Markov Chains Models, Statistical Monte Carlo Method Neural Networks, Computer Organ Dysfunction Scores Precision Medicine Sepsis Statistics, Nonparametric Treatment Outcome
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue 40017366
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Aged Algorithms APACHE Critical Illness Female Hospital Mortality Humans Intensive Care Units Machine Learning Male Organ Dysfunction Scores Prognosis Retrospective Studies
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Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

BMC infectious diseases 40269746
OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.
Aged Aged, 80 and over Delirium Female Humans Machine Learning Male Middle Aged Organ Dysfunction Scores Risk Factors ROC Curve Sepsis
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