AIMC Topic: Hospital Mortality

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Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

PLoS medicine Nov 30, 2018
BACKGROUND: Resuscitated cardiac arrest is associated with high mortality; however, the ability to estimate risk of adverse outcomes using existing illness severity scores is limited. Using in-hospital data available within the first 24 hours of admi...
Humans Female Time Factors Risk Factors Hospital Mortality Aged Machine Learning Treatment Outcome Male Decision Support Techniques Health Status Middle Aged Databases, Factual Australia Risk Assessment Retrospective Studies Clinical Decision-Making Registries New Zealand Cardiopulmonary Resuscitation Heart Arrest
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A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.

Health informatics journal Nov 29, 2018
We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients' first blood test results and physiologi...
Patient Admission Hospitalization Hospital Mortality ROC Curve Logistic Models Emergency Service, Hospital Humans Machine Learning
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Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Scientific reports Nov 20, 2018
Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning...
Humans Machine Learning Intensive Care Units Female Aged Aged, 80 and over Logistic Models Male Support Vector Machine Middle Aged Taiwan Hospital Mortality APACHE ROC Curve Airway Extubation Glasgow Coma Scale
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Vitamin D in the ICU: More sun for critically ill adult patients?

Nutrition (Burbank, Los Angeles County, Calif.) Nov 16, 2018
Critical illness in patients is characterized by systemic inflammation and oxidative stress. Vitamin D has a myriad of biological functions relevant to this population, including immunomodulation by the alteration of cytokine production and nuclear f...
Vitamin D Humans Critical Illness Vitamin D Deficiency Intensive Care Units Dietary Supplements Adult Vitamins Hospital Mortality Nutrition Therapy
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Optimal intensive care outcome prediction over time using machine learning.

PloS one Nov 14, 2018
BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions ...
Prognosis Retrospective Studies Intensive Care Units Clinical Decision-Making Female Aged Male Middle Aged Risk Assessment Sepsis Hospital Mortality Humans Machine Learning Datasets as Topic ROC Curve APACHE Decision Support Systems, Clinical Patient Admission Logistic Models Feasibility Studies Critical Care
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Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure.

Nutrition (Burbank, Los Angeles County, Calif.) Oct 24, 2018
OBJECTIVES: Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, this association in patients in the intensive care unit (ICU) has not been fully elucidated. The aim of this study was to investigate whether the serum...
Humans Vitamin D Prognosis Vitamin D Deficiency C-Reactive Protein Aged Critical Illness Infections Acute Disease Respiration, Artificial Adult Intensive Care Units Liver Failure Comorbidity Respiratory Insufficiency Female Cross-Sectional Studies Organ Dysfunction Scores Severity of Illness Index APACHE Male Patient Admission Time Factors Middle Aged Hospital Mortality Neoplasms
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Validation of deep-learning-based triage and acuity score using a large national dataset.

PloS one Oct 15, 2018
AIM: Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Sco...
Emergency Medicine ROC Curve Triage Area Under Curve Emergency Service, Hospital Risk Republic of Korea Female Reproducibility of Results Humans Aged Databases, Factual Adult Retrospective Studies Male Middle Aged Severity of Illness Index Deep Learning Cohort Studies Hospital Mortality
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Benchmarking deep learning models on large healthcare datasets.

Journal of biomedical informatics Jun 5, 2018
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist w...
Humans Neural Networks, Computer Algorithms Forecasting Databases, Factual Intensive Care Units Deep Learning Length of Stay Hospital Mortality Benchmarking International Classification of Diseases Female Aged Aged, 80 and over Male Middle Aged
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Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

Journal of child neurology May 13, 2018
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine ...
Electroencephalography Epilepsy Seizures Critical Care Neurophysiological Monitoring Infant, Newborn Young Adult Sensitivity and Specificity Male Diagnosis, Computer-Assisted Infant Humans Adolescent Machine Learning Child, Preschool Prognosis Child Female Hospital Mortality Area Under Curve
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The Dependence of Machine Learning on Electronic Medical Record Quality.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three a...
Los Angeles Data Accuracy Coronary Care Units Child Hospital Mortality Machine Learning Intensive Care Units, Pediatric Algorithms Logistic Models Humans Electronic Health Records Hospitals, Pediatric Infant Adolescent Child, Preschool
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