AIMC Topic: Risk Factors

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Machine-Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data.

Journal of child psychology and psychiatry, and allied disciplines
BACKGROUND: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.

Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.

Nature communications
The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on compl...

Association of Cardiovascular Mortality and Deep Learning-Funduscopic Atherosclerosis Score derived from Retinal Fundus Images.

American journal of ophthalmology
PURPOSE: The prediction of atherosclerosis using retinal fundus images and deep learning has not been shown possible. The purpose of this study was to develop a deep learning model which predicted atherosclerosis by using retinal fundus images and to...

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (i...

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

Journal of medical Internet research
BACKGROUND: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. With a rapid growth of the elderly population in recent years, lung cancer prevention and control are ...

Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction.

Annals of surgical oncology
BACKGROUND: Despite high success rates, flap failure remains an inherent risk in microvascular breast reconstruction. Identifying patients who are at high risk for flap failure would enable us to recommend alternative reconstructive techniques. Howev...

Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...