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Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.
Liu, Zheng; Han, Na; Su, Tao; Ji, Yuelong; Bao, Heling; Zhou, Shuang; Luo, Shusheng; Wang, Hui; Liu, Jue; Wang, Hai-Jun.
Affiliation
  • Liu Z; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Han N; Tongzhou Maternal and Child Health Care Hospital of Beijing, Beijing, China.
  • Su T; Tongzhou Maternal and Child Health Care Hospital of Beijing, Beijing, China.
  • Ji Y; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Bao H; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Zhou S; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Luo S; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Wang H; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
  • Liu J; Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
  • Wang HJ; Department of Maternal and Child Health, School of Public Health, Peking University, National Health Commission Key Laboratory of Reproductive Health, Beijing, China.
Front Pediatr ; 10: 899954, 2022.
Article in En | MEDLINE | ID: mdl-36440327
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Front Pediatr Year: 2022 Document type: Article Affiliation country: China Country of publication: Switzerland
Fulltext
Add to My VHL
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Front Pediatr Year: 2022 Document type: Article Affiliation country: China Country of publication: Switzerland