AIMC Topic: Risk Factors

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Machine Learning Prediction of Pancreatitis Risk With Antithyroid Drugs: A Nationwide Retrospective Observational Study.

The Journal of clinical endocrinology and metabolism
BACKGROUND: In recent years, there has been increasing data showing that the risk of acute pancreatitis (AP) is increased in patients using methimazole (MMI). The aim of this population-based study was to investigate the association between drugs use...

Development of an explainable machine learning model for predicting depression in adolescent girls with non-suicidal self-injury: A cross-sectional multicenter study.

Journal of affective disorders
Non-suicidal self-injury (NSSI) in adolescent girls is a critical predictor of subsequent depression and suicide risk, yet current tools lack both accuracy and clinical interpretability. We developed the first explainable machine learning model integ...

Development of a machine learning model for predicting cardiovascular risk factors and major adverse cardiovascular events in young adults with glucose metabolism disorders.

Medicina clinica
BACKGROUND: Glucose metabolism disorders (GMDs) are a serious global public health issue, characterized by a high incidence rate and youthfulness. GMDs contribute to the occurrence of major adverse cardiovascular events (MACEs), meanwhile with the re...

[Risk prediction of demoralization syndrome in patients with oral cancer].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology
OBJECTIVES: This study aimed to construct a risk prediction model for the occurrence of the demora-lization syndrome in patients with oral cancer and provide a scientific basis for the prevention of this syndrome in patients with oral cancer and the ...

Predicting Stress, Anxiety, and Depression in Adult Men Based on Nutritional and Lifestyle Variables: A Comparative Analysis of Machine Learning Methods.

Journal of food science
Mental health disorders like depression, anxiety, and stress (DAS) are rising globally. Understanding how diet and lifestyle influence these conditions is vital for targeted interventions. This study explores the potential of machine learning (ML) to...

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

Clinical transplantation
INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography...

Machine Learning Insights Into Amputation Risk: Evaluating Wound Classification Systems in Diabetic Foot Ulcers.

International wound journal
This study compares the performance of various wound classification systems to determine which system most effectively predicts amputation risk in diabetic foot ulcer (DFU) patients. Additionally, it identifies the key clinical and socioeconomic fact...

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

The American journal of psychiatry
OBJECTIVE: Postpartum depression (PPD) is a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. The auth...

Exploring the link between grandmaternal air pollution exposure and Grandchild's ASD risk: A multigenerational population-based study in California.

Environment international
BACKGROUND: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with increasing prevalence. While genetics play a strong causal role, among environmental factors, air pollution (AP) exposure in pregnancy and infancy has been strongly endo...

SSA-sMLP: A venous thromboembolism risk prediction model using separable self-attention and spatial-shift multilayer perceptrons.

Thrombosis research
Accurate risk assessment of Venous Thromboembolism (VTE) holds significant value for clinical decision-making. However, traditional scoring systems relying on linear assumptions and expert experience, along with machine learning models constrained by...