首页|Huazhong University of Science and Technology Reports Findings in Machine Learni ng (Predicting preterm birth using auto-ML frameworks: a large observational stu dy using electronic inpatient discharge data)
Huazhong University of Science and Technology Reports Findings in Machine Learni ng (Predicting preterm birth using auto-ML frameworks: a large observational stu dy using electronic inpatient discharge data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Hubei, People's Republ ic of China, by NewsRx editors, research stated, "To develop and compare differe nt AutoML frameworks and machine learning models to predict premature birth. The study used a large electronic medical record database to include 715,962 partic ipants who had the principal diagnosis code of childbirth." Our news journalists obtained a quote from the research from the Huazhong Univer sity of Science and Technology, "Three Automatic Machine Learning (AutoML) were used to construct machine learning models including tree-based models, ensembled models, and deep neural networks on the training sample ( = 536,971). The area under the curve (AUC) and training times were used to assess the performance of the prediction models, and feature importance was computed via permutation-shuff ling. The H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Autosklearn (median AUC: 0.820), and the medi an training time was the lowest for H2O AutoML (0.14 min), followed by AutoGluon (0.16 min) and Auto-sklearn (4.33 min). Among different types of machine learni ng models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XG Boost), stacked ensemble, and random forrest models had better predictive perfor mance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Impor tant features related to preterm birth included premature rupture of membrane (P ROM), incompetent cervix, occupation, and preeclampsia."
HubeiPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMedical RecordsRecords as TopicRisk and Prevention
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