Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data
BackgroundTo develop and compare different AutoML frameworks and machine learning models to predict premature birth.MethodsThe study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. Three Automatic Machine Learning (A...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-01-01
|
| Series: | Frontiers in Pediatrics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2024.1330420/full |
| _version_ | 1797338417439178752 |
|---|---|
| author | Deming Kong Ye Tao Haiyan Xiao Huini Xiong Weizhong Wei Miao Cai |
| author_facet | Deming Kong Ye Tao Haiyan Xiao Huini Xiong Weizhong Wei Miao Cai |
| author_sort | Deming Kong |
| collection | DOAJ |
| description | BackgroundTo develop and compare different AutoML frameworks and machine learning models to predict premature birth.MethodsThe study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. 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 (N = 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-shuffling.ResultsThe H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median 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 learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia.ConclusionsOur study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes. |
| first_indexed | 2024-03-08T09:29:56Z |
| format | Article |
| id | doaj.art-6148a7497c674cceba6c222d96964fb3 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-03-08T09:29:56Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-6148a7497c674cceba6c222d96964fb32024-01-31T04:37:06ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602024-01-011210.3389/fped.2024.13304201330420Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge dataDeming Kong0Ye Tao1Haiyan Xiao2Huini Xiong3Weizhong Wei4Miao Cai5Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaWuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaWuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaWuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaWuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, ChinaDepartment of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, ChinaBackgroundTo develop and compare different AutoML frameworks and machine learning models to predict premature birth.MethodsThe study used a large electronic medical record database to include 715,962 participants who had the principal diagnosis code of childbirth. 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 (N = 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-shuffling.ResultsThe H2O AutoML framework had the highest median AUC of 0.846, followed by AutoGluon (median AUC: 0.840) and Auto-sklearn (median AUC: 0.820), and the median 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 learning models, the Gradient Boosting Machines (GBM) or Extreme Gradient Boosting (XGBoost), stacked ensemble, and random forrest models had better predictive performance, with median AUC scores being 0.846, 0.846, and 0.842, respectively. Important features related to preterm birth included premature rupture of membrane (PROM), incompetent cervix, occupation, and preeclampsia.ConclusionsOur study highlights the potential of machine learning models in predicting the risk of preterm birth using readily available electronic medical record data, which have significant implications for improving prenatal care and outcomes.https://www.frontiersin.org/articles/10.3389/fped.2024.1330420/fullpreterm birthmachine learningadministrative dataChinaautoML |
| spellingShingle | Deming Kong Ye Tao Haiyan Xiao Huini Xiong Weizhong Wei Miao Cai Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data Frontiers in Pediatrics preterm birth machine learning administrative data China autoML |
| title | Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data |
| title_full | Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data |
| title_fullStr | Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data |
| title_full_unstemmed | Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data |
| title_short | Predicting preterm birth using auto-ML frameworks: a large observational study using electronic inpatient discharge data |
| title_sort | predicting preterm birth using auto ml frameworks a large observational study using electronic inpatient discharge data |
| topic | preterm birth machine learning administrative data China autoML |
| url | https://www.frontiersin.org/articles/10.3389/fped.2024.1330420/full |
| work_keys_str_mv | AT demingkong predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata AT yetao predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata AT haiyanxiao predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata AT huinixiong predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata AT weizhongwei predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata AT miaocai predictingpretermbirthusingautomlframeworksalargeobservationalstudyusingelectronicinpatientdischargedata |