AIMC Topic: Cause of Death

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Survival machine learning models for predicting all-cause and case-specific mortality risk in metabolic dysfunction-associated fatty liver disease patients.

Scientific reports
Emerging evidence links metabolic dysfunction-associated fatty liver disease (MAFLD) with increased all-cause and circulatory system disease (CSD) mortality in adults, yet survival machine learning studies are limited. This study analyzed 4415 NHANES...

Machine learning predicts lifespan and suggests underlying causes of death in aging C. elegans.

Communications biology
Aging leads to age-related pathology that causes death, and genes affect lifespan by determining such pathology. Here we investigate how age-related pathology mediates the effect of genetic and environmental interventions on lifespan in C. elegans by...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

Predicting All-Cause Mortality in Diabetic Patients 2 Years in Advance Using Aggregated EHR Data and Machine Learning.

Journal of medical systems
This study presents a machine learning-driven model predicting all-cause mortality two years in advance using administrative health data focused on diabetic patients. Integrating hospitalization records, emergency department data, demographics, and c...

Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model.

BMC cardiovascular disorders
BACKGROUND: Heart failure and atrial fibrillation (HF-AF) frequently coexist, resulting in complex interactions that substantially elevate mortality risk. This study aimed to develop and validate a machine learning (ML) model predicting the 3-year al...

A preliminary study on cause‑of‑death discrimination and the pathological stage identification in acute ischemia heart disease (AIHD) based on plasma lipidomic technique and machine learning algorithms.

International journal of legal medicine
The sudden death discrimination of acute ischemia heart disease (AIHD) and the determination of the AIHD pathological stage are the difficulties in forensic medicine. More potential biomarkers with high sensitivity and specificity still need to be id...

Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model.

BMC cancer
BACKGROUND: Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia ...

Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.

Frontiers in public health
BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.