AIMC Topic: Mortality

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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...
Humans Male Hospital Mortality Area Under Curve Machine Learning Critical Care Mortality Algorithms Female Reproducibility of Results
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Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

Nature medicine May 11, 2020
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event...
Algorithms Prognosis Retrospective Studies Female Humans Neural Networks, Computer Middle Aged Risk Assessment Deep Learning ROC Curve Electrocardiography Aged Aged, 80 and over Adult Male Cardiologists Area Under Curve Cause of Death Proportional Hazards Models Mortality
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Update to 'Deep-learning model for predicting 30-day postoperative mortality' (Br J Anaesth 2019; 123: 688-95).

British journal of anaesthesia May 7, 2020
Postoperative Period Mortality Humans Forecasting Deep Learning
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Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.

Scientific reports May 4, 2020
Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 20...
Models, Biological Mortality Peritoneal Dialysis Adult Humans Male Machine Learning Female Middle Aged Risk Factors Prospective Studies Predictive Value of Tests Aged Republic of Korea
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Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy.

Critical care (London, England) Feb 6, 2020
BACKGROUND: Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing co...
Organ Dysfunction Scores Mortality Risk Factors Aged Aged, 80 and over Male Continuous Renal Replacement Therapy ROC Curve Acute Kidney Injury Area Under Curve Republic of Korea APACHE Female Middle Aged Humans Severity of Illness Index Machine Learning Algorithms
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Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality.

International journal of environmental research and public health Jan 31, 2020
(1) Medical research has shown an increasing interest in machine learning, permitting massive multivariate data analysis. Thus, we developed drug intoxication mortality prediction models, and compared machine learning models and traditional logistic ...
Alcoholic Intoxication Neural Networks, Computer Machine Learning Logistic Models Republic of Korea Area Under Curve Risk Assessment Mortality
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Representation learning for clinical time series prediction tasks in electronic health records.

BMC medical informatics and decision making Dec 17, 2019
BACKGROUND: Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, n...
Heart Failure Forecasting Mortality Humans Electronic Health Records Neural Networks, Computer Comorbidity Machine Learning China Algorithms
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A deep learning model for real-time mortality prediction in critically ill children.

Critical care (London, England) Aug 14, 2019
BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine l...
Risk Assessment ROC Curve Infant Humans Pediatrics Deep Learning Area Under Curve Adolescent Critical Illness Retrospective Studies Big Data Mortality Cohort Studies Republic of Korea Child, Preschool Female Child Male
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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open Aug 10, 2019
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.
Adult Male Middle Aged Time Factors Registries Patient Discharge Mortality Logistic Models Emergency Service, Hospital Sweden Humans Aged Machine Learning Aged, 80 and over Retrospective Studies Young Adult Electronic Health Records Female
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Development and validation of 15-month mortality prediction models: a retrospective observational comparison of machine-learning techniques in a national sample of Medicare recipients.

BMJ open Jul 16, 2019
OBJECTIVE: The objective is to develop and validate a predictive model for 15-month mortality using a random sample of community-dwelling Medicare beneficiaries.
United States Aged Humans Medicare Aged, 80 and over Models, Statistical Neural Networks, Computer Mortality Male Machine Learning Decision Trees Logistic Models Female Bayes Theorem
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