AIMC Topic: Risk Factors

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Impact of heart failure on reoperation in adult congenital heart disease: An innovative machine learning model.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: The study objectives were to evaluate the association between preoperative heart failure and reoperative cardiac surgical outcomes in adult congenital heart disease and to develop a risk model for postoperative morbidity/mortality.

Establish and validate the reliability of predictive models in bone mineral density by deep learning as examination tool for women.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: While FRAX with BMD could be more precise in estimating the fracture risk, DL-based models were validated to slightly reduce the number of under- and over-treated patients when no BMD measurements were available. The validated models coul...

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of ...

Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning causal forest analysis.

The Journal of thoracic and cardiovascular surgery
OBJECTIVES: We aim to evaluate the heterogeneous treatment effects of coronary artery bypass grafting in patients with ischemic cardiomyopathy and to identify a group of patients to have greater benefits from coronary artery bypass grafting compared ...

Identifying Cardiovascular Disease Risk Factors in Adults with Explainable Artificial Intelligence.

Anatolian journal of cardiology
BACKGROUND: The aim of this study was to evaluate the relationship between risk factors causing cardiovascular diseases and their importance with explainable machine learning models.

Identifying multilevel predictors of trajectories of psychopathology and resilience among juvenile offenders: A machine learning approach.

Development and psychopathology
Mental ill health is more common among juvenile offenders relative to adolescents in general. Little is known about individual differences in their long-term psychological adaptation and its predictors from multiple aspects of their life. This study ...

CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the pr...

Using Machine Learning to Identify Predictors of Sexually Transmitted Infections Over Time Among Young People Living With or at Risk for HIV Who Participated in ATN Protocols 147, 148, and 149.

Sexually transmitted diseases
BACKGROUND: Sexually transmitted infections (STIs) among youth aged 12 to 24 years have doubled in the last 13 years, accounting for 50% of STIs nationally. We need to identify predictors of STI among youth in urban HIV epicenters.

Artificial intelligence-based approaches for suicide prediction: Hope or hype?

Asian journal of psychiatry
Accurate prediction of suicide risk is important because it allows evidence-based interventions to be targeted to at-risk populations. Conventional approaches to prediction of suicide risk have shown suboptimal accuracy. In this context, artificial i...

Prediction of suicidal ideation in children and adolescents using machine learning and deep learning algorithm: A case study in South Korea where suicide is the leading cause of death.

Asian journal of psychiatry
BACKGROUND: Korea has the highest suicide rate among Organisation for Economic Co-operation and Development (OECD) countries. Consequently, central and local governments and private organizations in Korea cooperate in promoting various suicide preven...