首页|Data on HIV/AIDS Reported by Zhihao Meng and Colleagues (Predictive model and ri sk analysis for coronary heart disease in people living with HIV using machine l earning)
Data on HIV/AIDS Reported by Zhihao Meng and Colleagues (Predictive model and ri sk analysis for coronary heart disease in people living with HIV using machine l earning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Immune System Diseases and Conditions - HIV/AIDS is the subject of a report. According to news origina ting from Guangxi, People’s Republic of China, by NewsRx correspondents, researc h stated, “This study aimed to construct a coronary heart disease (CHD) risk-pre diction model in people living with human immunodeficiency virus (PLHIV) with th e help of machine learning (ML) per electronic medical records (EMRs). Sixty-one medical characteristics (including demography information, laboratory measureme nts, and complicating disease) readily available from EMRs were retained for cli nical analysis.” Our news journalists obtained a quote from the research, “These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector m achine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression] . The performance of this model was assessed using the area under the receiver o perating characteristic curve (AUC). Shapley additive explanation (SHAP) was fur ther applied to interpret the findings of the best-performing model. The LightGB M model exhibited the highest AUC (0.849; 95% CI, 0.814-0.883). Ad ditionally, the SHAP plot per the LightGBM depicted that age, heart failure, hyp ertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and a mylase can help identify PLHIV who were at a high or low risk of developing CHD. This study developed a CHD risk prediction model for PLHIV utilizing ML techniq ues and EMR data. The LightGBM model exhibited improved comprehensive performanc e and thus had higher reliability in assessing the risk predictors of CHD.”
GuangxiPeople’s Republic of ChinaAsi aCardiologyCardiovascular Diseases and ConditionsCyborgsEmerging Technol ogiesHIV/AIDSHealth and MedicineHeart DiseaseHeart Disorders and Disease sImmune System Diseases and ConditionsMachine LearningPrimate LentivirusesRNA VirusesRetroviridaeRisk and PreventionVertebrate VirusesViral Sexu ally Transmitted Diseases and Conditions
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