AI Medical Compendium Topic:
Risk Assessment

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Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

International journal of medical informatics
INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of atherosclerosis, type 2 diabetes. The incidence of MetS significantly increases in postmenopausal wom...

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

BMC surgery
PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a use...

Development and multinational validation of an algorithmic strategy for high Lp(a) screening.

Nature cardiovascular research
Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under develo...

Accurate fall risk classification in elderly using one gait cycle data and machine learning.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...

RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning.

The Pediatric infectious disease journal
BACKGROUND: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pharmaceutical developments to be used during the prenatal and perinatal periods aimed at decreasing th...

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia.

Pacing and clinical electrophysiology : PACE
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in t...

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Frontiers in endocrinology
INTRODUCTION: Ultrasound is instrumental in the early detection of thyroid nodules, which is crucial for appropriate management and favorable outcomes. However, there is a lack of clinical guidelines for the judicious use of thyroid ultrasonography i...

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.

BMC infectious diseases
BACKGROUND: Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting...

Utilizing a comprehensive machine learning approach to identify patients at high risk for extended length of stay following spinal deformity surgery in pediatric patients with early onset scoliosis.

Spine deformity
PURPOSE: Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery.