AI Medical Compendium Topic:
Cohort Studies

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Palliative Care Exposure Relative to Predicted Risk of Six-Month Mortality in Hospitalized Adults.

Journal of pain and symptom management
CONTEXT: The optimal strategy for implementing mortality-predicting algorithms to facilitate clinical care, prognostic discussions, and palliative care interventions remains unknown.

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

American journal of human genetics
In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapi...

A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study.

PloS one
Anthropometry is a Greek word that consists of the two words "Anthropo" meaning human species and "metery" meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and ...

Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features.

Fertility and sterility
OBJECTIVE: To assess the best-performing machine learning (ML) model and features to predict euploidy in human embryos.

A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.

Neuroradiology
PURPOSE: This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.

Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study.

The Journal of hospital infection
BACKGROUND: Urinary tract infection (UTI) is one of major nosocomial infections significantly affecting the outcomes of immobile stroke patients. Previous studies have identified several risk factors, but it is still challenging to accurately estimat...

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD b...

Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.

Scientific reports
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the...

Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.

Fertility and sterility
OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the second...

Translatability Analysis of National Institutes of Health-Funded Biomedical Research That Applies Artificial Intelligence.

JAMA network open
IMPORTANCE: Despite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice.