AIMC Topic: Patient Admission

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Machine learning based early mortality prediction in the emergency department.

International journal of medical informatics Sep 8, 2021
BACKGROUND: It is a great challenge for emergency physicians to early detect the patient's deterioration and prevent unexpected death through a large amount of clinical data, which requires sufficient experience and keen insight.
Electronic Health Records Female Child Emergency Service, Hospital Patient Discharge Patient Admission Humans Machine Learning
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Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran.

Scientific reports Aug 20, 2021
This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 year...
Child, Preschool Child Air Pollutants Air Pollution Seasons Iran Male Cardiovascular Diseases Middle Aged Infant Adolescent Time Factors Linear Models Multivariate Analysis Patient Admission Geography Humans Neural Networks, Computer Female Infant, Newborn Hospitalization Aged Young Adult Adult
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Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions.

JAMA network open Aug 2, 2021
IMPORTANCE: Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valu...
Humans Machine Learning Retrospective Studies Tertiary Care Centers Benchmarking Patient Acuity Patient Admission Middle Aged Risk Assessment Predictive Value of Tests Triage Emergency Service, Hospital Singapore Female Reproducibility of Results Risk Factors Aged Male
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Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

PLoS computational biology Jun 23, 2021
Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guid...
Logistic Models Patient Admission Aged, 80 and over Young Adult Adult Male Middle Aged Infant Comorbidity Severity of Illness Index England Adolescent Child, Preschool Child Retrospective Studies Intensive Care Units Female SARS-CoV-2 Risk Factors Aged Humans Machine Learning COVID-19
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Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

Scientific reports Jun 17, 2021
In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individu...
Prognosis Retrospective Studies Electronic Health Records Female SARS-CoV-2 Risk Factors Humans Machine Learning COVID-19 Prospective Studies Aged Male Middle Aged Risk Assessment India Triage Proportional Hazards Models Propensity Score Patient Admission
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Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Scientific reports Apr 23, 2021
Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve res...
Humans Neural Networks, Computer Intensive Care Units Female Reproducibility of Results Aged Gastrointestinal Hemorrhage Patient Admission Erythrocyte Transfusion Aged, 80 and over Adult Male Middle Aged Risk Assessment Cohort Studies
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Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study.

BJOG : an international journal of obstetrics and gynaecology Apr 15, 2021
OBJECTIVE: To create a personalised machine learning model for prediction of severe adverse neonatal outcomes (SANO) during the second stage of labour.
Pregnancy Female Parity Adult ROC Curve Pregnancy Outcome Predictive Value of Tests Patient Admission Delivery, Obstetric Obstetric Labor Complications Gravidity Labor Presentation Labor Stage, First Labor Stage, Second Humans Machine Learning Retrospective Studies
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Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study.

PloS one Mar 31, 2021
BACKGROUND: Healthcare associated infections (HAI) are a major burden for the healthcare system and associated with prolonged hospital stay, increased morbidity, mortality and costs. Healthcare associated urinary tract infections (HA-UTI) accounts fo...
Length of Stay ROC Curve Denmark Risk Factors Urinary Tract Infections Aged Cross Infection Humans Young Adult Patient Admission Machine Learning Adult Retrospective Studies Male Electronic Health Records Middle Aged Female
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A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Journal of medical Internet research Feb 10, 2021
BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk f...
Emergency Service, Hospital Respiratory Insufficiency Patient Admission Intubation, Intratracheal Early Warning Score Clinical Decision Rules Middle Aged Respiration, Artificial Hospitals ROC Curve Logistic Models Triage Female SARS-CoV-2 Hospitalization Aged Male Humans Machine Learning COVID-19 Retrospective Studies
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Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

JAMA network open Feb 1, 2021
IMPORTANCE: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators ...
Middle Aged Infant Adolescent Humans Child, Preschool Machine Learning Child Prospective Studies ROC Curve Computer Simulation Infant, Newborn Retrospective Studies Reproducibility of Results Female Young Adult Adult Male Triage Area Under Curve Whole Genome Sequencing Patient Selection Referral and Consultation Patient Admission Rare Diseases Undiagnosed Diseases
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