AIMC Topic: Organ Dysfunction Scores

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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 Feb 1, 2025
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...
Humans Machine Learning Algorithms Aged Male Hospital Mortality APACHE Organ Dysfunction Scores Prognosis Retrospective Studies Critical Illness Intensive Care Units Female
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Individualized multi-treatment response curves estimation using RBF-net with shared neurons.

Biometrics Jan 7, 2025
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...
Biometry Humans Statistics, Nonparametric Neural Networks, Computer Algorithms Models, Statistical Computer Simulation Bayes Theorem Precision Medicine Markov Chains Intensive Care Units Organ Dysfunction Scores Length of Stay Monte Carlo Method Sepsis Treatment Outcome
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Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Renal failure Dec 1, 2022
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 ...
Humans Machine Learning Databases, Factual Retrospective Studies Organ Dysfunction Scores China Acute Kidney Injury ROC Curve Female Logistic Models Aged Cerebrovascular Disorders Male Critical Care Middle Aged
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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 Aug 1, 2021
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 ...
Retrospective Studies Adult Male Middle Aged Risk Assessment Humans Clinical Deterioration Deep Learning Sepsis Critical Care Organ Dysfunction Scores Pennsylvania
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A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

The journal of trauma and acute care surgery Oct 1, 2020
BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We...
Multivariate Analysis Injury Severity Score Organ Dysfunction Scores Wounds and Injuries Prognosis Hospitalization Young Adult Retrospective Studies Sepsis Adult Critical Illness ROC Curve Male Intensive Care Units Sodium Middle Aged Female Critical Care Humans Proportional Hazards Models Neural Networks, Computer Machine Learning
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On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the ...
Diagnosis, Differential Organ Dysfunction Scores Humans Machine Learning Intensive Care Units Electronic Health Records Sensitivity and Specificity Sepsis
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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine Feb 1, 2019
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Humans Emergency Service, Hospital Machine Learning Singapore Heart Rate Hospital Mortality Female Systemic Inflammatory Response Syndrome Reproducibility of Results Organ Dysfunction Scores Aged, 80 and over ROC Curve Aged Male Socioeconomic Factors Middle Aged Triage Severity of Illness Index Sepsis
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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Oct 1, 2018
OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children.
Monitoring, Physiologic Predictive Value of Tests Heart Rate Organ Dysfunction Scores Respiratory Rate Sepsis Adolescent Child Intensive Care Units, Pediatric Logistic Models Machine Learning Prospective Studies Humans Female Artificial Intelligence Male Case-Control Studies
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[Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].

Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns Jun 20, 2018
To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. The clinical data of 157 severely burned patients in Augu...
Humans Machine Learning Acute Kidney Injury Burns Hospitalization Organ Dysfunction Scores Sensitivity and Specificity Explosions Sepsis ROC Curve Logistic Models Fluid Therapy
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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Critical care medicine Apr 1, 2018
OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-ti...
Vital Signs Female Severity of Illness Index Humans Heart Rate Decision Support Systems, Clinical Aged Sepsis Machine Learning Sex Factors Socioeconomic Factors Male Time Factors Critical Illness Academic Medical Centers Electrocardiography Middle Aged Hospital Mortality Intensive Care Units Time-to-Treatment Age Factors Comorbidity ROC Curve Electronic Health Records Organ Dysfunction Scores Blood Pressure
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