AIMC Topic: Risk Factors

Clear Filters Showing 1161 to 1170 of 2857 articles

Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Frontiers in public health
INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to p...

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease.

Frontiers in endocrinology
INTRODUCTION: Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve pati...

A machine learning analysis of predictors of future hypertension in a young population.

Minerva cardiology and angiology
BACKGROUND: Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models.

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.

BMC medical informatics and decision making
OBJECTIVE: Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction ...

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

The Journal of arthroplasty
BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alter...

Predicting kidney allograft survival with explainable machine learning.

Transplant immunology
INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning meth...

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Nature medicine
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopa...

A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Psychiatry research
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this conditi...

Preoperatively predicting survival outcome for clinical stage IA pure-solid non-small cell lung cancer by radiomics-based machine learning.

The Journal of thoracic and cardiovascular surgery
OBJECTIVE: Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical ...