AI Medical Compendium Topic:
Risk Factors

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Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

Circulation. Cardiovascular imaging
BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We...

Predicting Individual Treatment Effects to Determine Duration of Dual Antiplatelet Therapy After Stent Implantation.

Journal of the American Heart Association
BACKGROUND: After coronary stent implantation, prolonged dual antiplatelet therapy (DAPT) increases bleeding risk, requiring personalization of DAPT duration. The aim of this study was to develop and validate a machine learning model to predict optim...

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.

PloS one
Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuabl...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

Artificial intelligence in medicine
BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted...

Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.

Journal of thrombosis and thrombolysis
To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CTPA) images for short-term adverse outcomes in patients with acute pulmonary embolism (APE). T...

Machine-learning based prediction model for acute kidney injury induced by multiple wasp stings.

Toxicon : official journal of the International Society on Toxinology
Acute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop a...

Visualization obesity risk prediction system based on machine learning.

Scientific reports
Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and progression are required. In this study, we developed a visualized obesity risk prediction system b...

Identifying predictors of multi-year cannabis vaping in U.S. Young adults using machine learning.

Addictive behaviors
INTRODUCTION: Increasing number of current cannabis users report using a vaporized form of cannabis and young adults are most likely to vape cannabis. However, the number of studies on cannabis vaping is limited, and predictors of cannabis vaping amo...

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.

BMC medical research methodology
BACKGROUND: Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data...

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Scientific reports
Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinica...