AI Medical Compendium Topic:
Cohort Studies

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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)
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...

Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.

BMC pregnancy and childbirth
BACKGROUND: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, ad...

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

Annals of the New York Academy of Sciences
This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used ...

Identifying the predictive effectiveness of a genetic risk score for incident hypertension using machine learning methods among populations in rural China.

Hypertension research : official journal of the Japanese Society of Hypertension
Current studies have shown the controversial effect of genetic risk scores (GRSs) in hypertension prediction. Machine learning methods are used extensively in the medical field but rarely in the mining of genetic information. This study aims to deter...

Deep Learning for Detection of Pulmonary Metastasis on Chest Radiographs.

Radiology
Background A computer-aided detection (CAD) system may help surveillance for pulmonary metastasis at chest radiography in situations where there is limited access to CT. Purpose To evaluate whether a deep learning (DL)-based CAD system can improve di...

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

PloS one
OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. ...

NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases.

Human genetics
Syndromic retinal diseases (SRDs) are a group of complex inherited systemic disorders, with challenging molecular underpinnings and clinical management. Our main goal is to improve clinical and molecular SRDs diagnosis, by applying a structured pheno...

Deep neural network-estimated electrocardiographic age as a mortality predictor.

Nature communications
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A d...

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Scientific reports
Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of individuals who had TIA and measured changes in their abilities to perform behavioural tasks over 1 ...