AIMC Topic: Hospital Mortality

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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association Mar 5, 2019
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...
Risk Factors Aged Male Stroke Volume Humans Machine Learning Databases, Factual Female Registries Percutaneous Coronary Intervention Coronary Artery Disease Data Mining New York Middle Aged Hospital Mortality Treatment Outcome Age Factors
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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 Machine Learning Hospital Mortality Systemic Inflammatory Response Syndrome ROC Curve Socioeconomic Factors Singapore Heart Rate Organ Dysfunction Scores Triage Emergency Service, Hospital Male Middle Aged Severity of Illness Index Sepsis Female Reproducibility of Results Aged Aged, 80 and over
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Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Anesthesiology Oct 1, 2018
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.
Neural Networks, Computer Machine Learning Female Aged Humans Hospital Mortality ROC Curve Predictive Value of Tests Adult Male Middle Aged Postoperative Complications
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Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Clinical scoring systems have been developed for many specific applications, yet they remain underutilized for common reasons such as model inaccuracy and difficulty of use. For intensive care units specifically, the Acute Physiology and Chronic Heal...
Intensive Care Units Severity of Illness Index Hospital Mortality Humans APACHE Machine Learning
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Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation.

The American surgeon Jul 1, 2018
The purpose of this study was to use natural language processing of physician documentation to predict mortality in patients admitted to the surgical intensive care unit (SICU). The Multiparameter Intelligent Monitoring in Intensive Care III database...
Documentation General Surgery Humans United States Artificial Intelligence Predictive Value of Tests Retrospective Studies Natural Language Processing Length of Stay Intensive Care Units Critical Care Electronic Health Records Hospital Mortality Severity of Illness Index ROC Curve
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Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

Critical care medicine Jul 1, 2018
OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, ...
Hospital Mortality Natural Language Processing Patient Care Planning Decision Support Techniques Retrospective Studies Intensive Care Units Female Aged Male Middle Aged Length of Stay Humans Machine Learning
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Mortality Prediction Gets a "Boost".

Critical care medicine Jun 1, 2018
Algorithms Hospital Mortality Critical Care Risk Adjustment Humans Machine Learning
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Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

Critical care medicine Jun 1, 2018
OBJECTIVES: Risk adjustment algorithms for ICU mortality are necessary for measuring and improving ICU performance. Existing risk adjustment algorithms are not widely adopted. Key barriers to adoption include licensing and implementation costs as wel...
Humans Algorithms Intensive Care Units Female Male Middle Aged Hospital Mortality Models, Statistical Unsupervised Machine Learning Risk Adjustment
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[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences Apr 18, 2018
OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) w...
Critical Care Sensitivity and Specificity Hospital Mortality Humans ROC Curve Prognosis Support Vector Machine Acute Kidney Injury Intensive Care Units
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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...
Organ Dysfunction Scores Vital Signs Sepsis Blood Pressure Decision Support Systems, Clinical Time Factors Male Sex Factors Heart Rate Socioeconomic Factors Hospital Mortality Humans Middle Aged Academic Medical Centers Intensive Care Units Electrocardiography ROC Curve Machine Learning Comorbidity Electronic Health Records Age Factors Time-to-Treatment Severity of Illness Index Critical Illness Female Aged
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