AIMC Topic: Cause of Death

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Machine Learning and Prediction of All-Cause Mortality in COPD.

Chest Apr 27, 2020
BACKGROUND: COPD is a leading cause of mortality.
Male Middle Aged Predictive Value of Tests Cause of Death Respiratory Function Tests Humans Machine Learning Female Pulmonary Disease, Chronic Obstructive
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Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm.

Journal of biophotonics Jan 27, 2020
This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural net...
Algorithms Deep Learning Cause of Death Pulmonary Edema Autopsy Humans
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Automatically determining cause of death from verbal autopsy narratives.

BMC medical informatics and decision making Jul 9, 2019
BACKGROUND: A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily u...
Natural Language Processing Cause of Death Diagnostic Techniques and Procedures Young Adult Humans Infant Adult Machine Learning Adolescent Male Female Middle Aged Child, Preschool Infant, Newborn Child Aged Narration
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Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.

International journal of medical informatics Jul 6, 2019
BACKGROUND: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveil...
Support Vector Machine Public Health Surveillance Cause of Death Supervised Machine Learning France Humans Adult Male Classification Disease Epidemiological Monitoring Death Certificates
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Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.

ESC heart failure Feb 27, 2019
AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results...
Follow-Up Studies Cause of Death Heart Failure Patient Readmission Quality Indicators, Health Care Survival Rate Male Time Factors ROC Curve Western Australia Humans Machine Learning Retrospective Studies Female Aged Aged, 80 and over
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Constipation and risk of death and cardiovascular events.

Atherosclerosis Dec 23, 2018
BACKGROUND AND AIMS: Constipation is one of the most frequent symptoms encountered in daily clinical practice and is implicated in the development of atherosclerosis, potentially through altered gut microbiota. However, little is known about its asso...
Laxatives Risk Assessment Risk Factors Humans Incidence Cause of Death Aged Prognosis Time Factors Stroke Male Retrospective Studies United States Brain Ischemia Female Middle Aged Coronary Disease Constipation
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Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Artificial intelligence in medicine May 10, 2018
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality ...
Rare Diseases New South Wales Death Certificates Cause of Death Registries Reproducibility of Results Data Mining Neoplasms Data Accuracy Humans Support Vector Machine Databases, Factual Natural Language Processing
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Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.

JACC. Cardiovascular imaging Apr 18, 2018
OBJECTIVES: This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significa...
Humans Machine Learning Phenotype Retrospective Studies Female Risk Factors Hospitalization Aged Aged, 80 and over Progression-Free Survival Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Pattern Recognition, Automated Diastole Echocardiography, Doppler, Color Disease Progression Male Echocardiography, Doppler, Pulsed Cluster Analysis Ventricular Function, Left Predictive Value of Tests Middle Aged Cause of Death Risk Assessment Heart Ventricles
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Predictors of firearm violence in urban communities: A machine-learning approach.

Health & place Mar 14, 2018
Interpersonal firearm violence is a leading cause of death and injuries in the United States. Identifying community characteristics associated with firearm violence is important to improve confounder selection and control in health research, to bette...
Machine Learning Female Violence Male Urban Population Humans Adolescent Age Distribution Cause of Death United States Poverty Areas Ethnicity Sex Distribution Firearms
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Risk prediction model for in-hospital mortality in women with ST-elevation myocardial infarction: A machine learning approach.

Heart & lung : the journal of critical care Oct 6, 2017
BACKGROUND: Studies had shown that mortality due to ST-elevation myocardial infarction (STEMI) is higher in women compared with men. The purpose of this study is to develop and validate prediction models for all-cause in-hospital mortality in women a...
Retrospective Studies Female Aged Risk Assessment Hospital Mortality Humans Machine Learning Prognosis United States ST Elevation Myocardial Infarction Cause of Death
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