AIMC Topic: Hospital Mortality

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Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

BMC medical informatics and decision making Oct 2, 2020
BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predi...
Humans Machine Learning Critical Illness Male Middle Aged Sepsis Hospital Mortality Models, Theoretical Critical Care Intensive Care Units Female Aged Aged, 80 and over Adult
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Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

The Annals of pharmacotherapy Sep 15, 2020
INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship be...
APACHE Medication Reconciliation Adult Humans Male Machine Learning Middle Aged Retrospective Studies Hospital Mortality Bayes Theorem Hospitalization Aged Aged, 80 and over Critical Illness Intensive Care Units Female
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Supervised classification techniques for prediction of mortality in adult patients with sepsis.

The American journal of emergency medicine Sep 12, 2020
BACKGROUND: Sepsis mortality is still unacceptably high and an appropriate prognostic tool may increase the accuracy for clinical decisions.
Humans Artificial Intelligence Prospective Studies Prognosis Colombia Organ Dysfunction Scores Hospital Mortality Decision Making Predictive Value of Tests Emergency Service, Hospital Hospitals, University Female Aged Male Middle Aged Sepsis
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Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study.

The European respiratory journal Aug 20, 2020
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality.
Predictive Value of Tests Triage Area Under Curve Italy Coronavirus Infections Cohort Studies Hospital Mortality ROC Curve Decision Support Systems, Clinical Age Factors Clinical Laboratory Techniques Pneumonia, Viral COVID-19 Testing Sex Factors Survival Analysis Internationality Belgium Female Reproducibility of Results Hospitalization Aged Adult Male Middle Aged Risk Assessment Severity of Illness Index China Humans Machine Learning Pandemics COVID-19 Retrospective Studies
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Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

The American journal of medicine Aug 15, 2020
BACKGROUND: General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learnin...
Middle Aged Big Data Adolescent Hospital Mortality Inpatients Young Adult Humans Risk Factors Adult Machine Learning Aged Male Female Aged, 80 and over
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Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

Nutrition, metabolism, and cardiovascular diseases : NMCD Jul 31, 2020
BACKGROUND AND AIMS: There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisp...
Humans Betacoronavirus Glomerular Filtration Rate Pneumonia, Viral SARS-CoV-2 Age Factors Risk Factors Machine Learning C-Reactive Protein Male Aged Pandemics Aged, 80 and over Coronavirus Infections Cardiovascular Diseases COVID-19 Survival Analysis Retrospective Studies Young Adult Middle Aged Female Adult Adolescent Hospital Mortality
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Prediction on critically ill patients: The role of "big data".

Journal of critical care Jul 23, 2020
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...
Humans Machine Learning Delivery of Health Care Electronic Health Records Severity of Illness Index Length of Stay Prognosis Critical Illness Intensive Care Units Hospital Mortality Critical Care
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A machine learning approach for mortality prediction only using non-invasive parameters.

Medical & biological engineering & computing Jul 20, 2020
At present, the traditional scoring methods generally utilize laboratory measurements to predict mortality. It results in difficulties of early mortality prediction in the rural areas lack of professional laboratorians and medical laboratory equipmen...
Critical Care Mortality Female Humans Reproducibility of Results Machine Learning Male Algorithms Hospital Mortality Area Under Curve
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Benchmarking machine learning models on multi-centre eICU critical care dataset.

PloS one Jul 2, 2020
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and publi...
Humans Machine Learning Software Algorithms Length of Stay Hospital Mortality Benchmarking Critical Care Datasets as Topic Clinical Decision Rules
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Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

Clinical research in cardiology : official journal of the German Cardiac Society Jun 24, 2020
BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indicat...
Morbidity Heart Valve Diseases Germany Transcatheter Aortic Valve Replacement Time Factors Hospital Mortality Aortic Valve Humans Aged, 80 and over Inpatients Machine Learning Survival Rate Cause of Death Retrospective Studies Male Female Risk Assessment Risk Factors Postoperative Complications
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