AIMC Topic: Hospital Mortality

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Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

Medical sciences (Basel, Switzerland) Sep 24, 2021
BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.
Acute Kidney Injury Female Hospitalization Aged Humans Machine Learning Hospital Mortality Consensus Cluster Analysis Chlorides Aged, 80 and over Adult Male Middle Aged Bicarbonates
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Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Scientific reports Sep 23, 2021
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...
Hemorrhage Treatment Failure Ischemic Attack, Transient Retrospective Studies Risk Factors Humans Risk Assessment Aged Machine Learning Aged, 80 and over Hospital Mortality Critical Illness Male Stroke Intensive Care Units Female Atrial Fibrillation
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Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.

Critical care (London, England) Sep 8, 2021
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate...
Humans Machine Learning COVID-19 Prognosis Respiration, Artificial Severity of Illness Index Cohort Studies Hospital Mortality Female Risk Factors Male Risk Assessment
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Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction.

IEEE transactions on neural networks and learning systems Aug 31, 2021
Electronic health records (EHRs) are characterized as nonstationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missi...
Uncertainty Neural Networks, Computer Algorithms Electronic Health Records Deep Learning Hospital Mortality Computational Biology Predictive Value of Tests Stochastic Processes Humans
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Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

Scientific reports Aug 2, 2021
The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the c...
Hospital Mortality Socioeconomic Factors Models, Biological Hospitalization Male Brazil Humans Machine Learning Pandemics COVID-19 Female SARS-CoV-2
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Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.

PloS one Aug 2, 2021
BACKGROUND: Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.
Time Factors Reproducibility of Results Hospital Mortality Risk Factors Humans Area Under Curve ROC Curve Male Machine Learning ST Elevation Myocardial Infarction Middle Aged Thrombosis Female Asian People
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Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

PloS one Jun 29, 2021
BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostom...
Aged Models, Biological Deep Learning Male Time Factors Humans Middle Aged Hospital Mortality Intensive Care Units Respiration, Artificial Predictive Value of Tests Female
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Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.

Scientific reports Jun 18, 2021
Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of...
Aged Middle Aged Risk Assessment Time Factors Hospital Mortality ROC Curve Models, Theoretical Non-ST Elevated Myocardial Infarction Humans Machine Learning Reproducibility of Results Risk Factors Area Under Curve
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Machine ​learning algorithms for claims data-based prediction of in-hospital mortality in patients with heart failure.

ESC heart failure Jun 4, 2021
AIMS: Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in-hospital mortality rates in HF cohorts o...
Hospital Mortality Heart Failure Algorithms Female Humans Hospitalization Machine Learning Male
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OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

BMC medical informatics and decision making May 13, 2021
BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for q...
Retrospective Studies Intensive Care Units Humans Hospital Mortality Machine Learning Prognosis
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