AIMC Topic: Risk Factors

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Explainable ensemble learning for Epstein-Barr virus risk prediction in ulcerative colitis and Crohn's disease using routine biomarkers.

Scientific reports
Epstein-Barr virus (EBV) exacerbates inflammatory bowel disease (IBD) and is challenging to monitor with invasive or costly tests. We investigated whether explainable machine learning can predict EBV infection from routine clinical data in ulcerative...

Opportunistic screening of type 2 diabetes with deep metric learning using electronic health records.

Scientific reports
Deep learning models leveraging electronic health records (EHR) for opportunistic screening of type 2 diabetes (T2D) can improve current practices by identifying individuals who may need further glycemic testing. Accurate onset prediction and subtypi...

Novel insights into predicting the presence of micropapillary and solid components in stage IA lung adenocarcinoma using machine learning models of modifiable risk factors.

Annals of medicine
BACKGROUND: Lung adenocarcinoma (LUAC) patients with micropapillary (MP) and/or solid (S) generally demonstrate a poorer survival prognosis. In the diagnosis and treatment of stage IA LUAC, precisely establishing personalized treatment strategies for...

Higher hospital level does not improve 30-day survival after road traffic accidents.

Scientific reports
Globally, road traffic accidents (RTAs) remain a major cause of death, particularly among individuals aged 15-30 years. While Sweden has been at the forefront of traffic safety through the Vision Zero initiative, in-hospital management remains crucia...

Application of generalized linear mixed effects random forest for identifying risk factors of prediabetes in Tehran Lipid and Glucose Study.

Scientific reports
Prediabetes is a major risk factor for the development of diabetes, defined by blood glucose levels that are elevated but not yet high enough to meet the diagnostic criteria for Diabetes Mellitus. This condition is often clinically "silent" yet it ca...

Predicting the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures using machine learning algorithms.

PloS one
OBJECTIVE: To construct and validate a predictive model for the risk of postoperative constipation in middle-aged and elderly patients with lower limb fractures based on machine learning algorithms, so as to provide decision-making support for clinic...

Predicting proximal junctional failure in adult spinal deformity patients using machine learning models based on spinal alignment parameters.

Scientific reports
Proximal junctional failure (PJF) is a significant mechanical complication following corrective surgery for adult spinal deformity (ASD), often resulting in structural failure at the uppermost instrumented vertebra and necessitating revision surgery....

Face2Bone explainable AI model predicts osteoporosis risk from facial images in proof of concept study.

Scientific reports
OBJECTIVES: BMI and age are associated with the risk of osteoporosis (OP). The dynamic facial aging process involves changes in skin, muscle, fat, and facial bone structures, with facial skeletal aging affecting facial contours through volumetric red...

Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry.

Nature communications
Prostate cancer (PrCa) incidence and severity vary across ancestries; men of African ancestry (AA) are more likely to be diagnosed and die from PrCa than those of European ancestry (EA). Current polygenic risk scores, even from multi-ancestry GWAS, d...

Plasma metabolomics disentangles T2DM- and CAD-specific dysmetabolism and identifies potential biomarkers for CAD risk escalation in diabetic patients.

Cardiovascular diabetology
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a major driver of coronary artery disease (CAD). Prior studies often conflate T2DM- and CAD-specific metabolic alterations, limiting insights into CAD pathogenesis in T2DM. This study aimed to distinguis...