AIMC Topic: Risk Assessment

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Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

European journal of medical research
BACKGROUND: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Development and Validation of a Nomogram for Predicting Frailty Risk Among Older Patients With Ischaemic Stroke.

Journal of clinical nursing
AIM: To investigate the risk factors associated with frailty in older patients with ischaemic stroke, develop a nomogram and apply it clinically.

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.

International journal of cardiology
BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead to varying degrees of bleeding, resulting in serious clinical events, including death. This study ai...

Predicting preterm birth using electronic medical records from multiple prenatal visits.

BMC pregnancy and childbirth
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...

Machine learning based prediction model for bile leak following hepatectomy for liver cancer.

HPB : the official journal of the International Hepato Pancreato Biliary Association
OBJECTIVE: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.

Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.

BMC public health
BACKGROUND: Given the rapid increase in the prevalence of prostate cancer (PCa), identifying its risk factors and developing suitable risk prediction models has important implications for public health. We used machine learning (ML) approach to scree...

Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.

BMC cardiovascular disorders
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools...

Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist, there is a lack of predictive tools to guide surgical decisions for hepatic CE. This study aimed to...