{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T16:39:34Z","timestamp":1726331974473},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4152007"],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China National Key Technology Research and Development Program projects","award":["2013BAH05F02","2015BAH13F01"]},{"name":"Guangdong Key Laboratory of Popular High Performance Computers"},{"name":"Shenzhen Key Laboratory of Service Computing and Applications"},{"name":"Beijing Chaoyang Collaborative Innovation project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2020,6,1]]},"DOI":"10.1109\/tbdata.2016.2620981","type":"journal-article","created":{"date-parts":[[2016,10,31]],"date-time":"2016-10-31T18:08:10Z","timestamp":1477937290000},"page":"334-346","source":"Crossref","is-referenced-by-count":20,"title":["Comparison of Different Machine Learning Approaches to Predict Small for Gestational Age Infants"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-1995-9249","authenticated-orcid":false,"given":"Jianqiang","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0722-9241","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jingchao","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Haowen","family":"Mo","sequence":"additional","affiliation":[]},{"given":"Ji-Jiang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Huiting","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Pan","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.114"},{"key":"ref32","first-page":"1","article-title":"Machine learning benchmarks and random forest regression","author":"segal","year":"2004","journal-title":"Biostatistics"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-11-51"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0033812"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1159\/000321694"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/uog.14771"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2015.01.012"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1111\/j.1526-4637.2011.01228.x"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2012.11.008"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2015.10.007"},{"key":"ref16","volume":"680","author":"duda","year":"2001","journal-title":"Pattern Classification"},{"key":"ref17","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"fan","year":"2008","journal-title":"J Mach Learn"},{"key":"ref18","first-page":"1","article-title":"Random forests","volume":"45","author":"breiman","year":"2001","journal-title":"Statistics (Ber )"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511815867"},{"key":"ref28","article-title":"IBM SPSS Statistics for Windows, Version 22.0","year":"2015"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1542\/peds.112.2.301"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1542\/peds.37.3.417","article-title":"Birth weight, gestational age, and pregnancy outcome, with special reference to high birth weight-low gestational age infant","volume":"37","author":"battaglia","year":"1966","journal-title":"Pediatrics"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/uog.1806"},{"key":"ref29","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learning Res"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1159\/000091509"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/uog.14822","article-title":"Umbilical and fetal middle cerebral artery Doppler at 30-34 weeks’ gestation in the prediction of adverse perinatal outcome","volume":"45","author":"nicolaides","year":"2015","journal-title":"Ultrasound Obstet Gynecol"},{"key":"ref7","first-page":"730","article-title":"Prediction of the small for gestational age twin fetus by Doppler umbilical artery waveform analysis","volume":"74","author":"hastie","year":"1989","journal-title":"Obstetrics Gynecology"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1542\/peds.111.6.1253"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-015-0461-z"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-3476(67)80066-0"},{"key":"ref20","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref22","first-page":"169","article-title":"The assessment criteria of pre-pregnancy risk classification for couples of childbearing age","volume":"95","author":"shen","year":"2015","journal-title":"National Medical Journal of China"},{"key":"ref21","first-page":"162","article-title":"Design, implementation and significance of Chinese free Pre-pregnancy Eugenics Checks Project","volume":"95","author":"zhang","year":"2015","journal-title":"National Medical Journal of China"},{"key":"ref24","article-title":"C4.5: Programs for machine learning","volume":"240","author":"quinlan","year":"1993","journal-title":"Mach Learn"},{"key":"ref23","first-page":"97","article-title":"Chinese neonatal birth weight curve for different gestational age","volume":"53","author":"zhu","year":"2015","journal-title":"Chin J Pediatr"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"ref25","first-page":"901","article-title":"Combining knowledge and data driven insights for identifying risk factors using electronic health records","volume":"2012","author":"sun","year":"0","journal-title":"Proc AMIA Annu Symp"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/9098178\/07725951.pdf?arnumber=7725951","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T21:42:30Z","timestamp":1657575750000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/7725951\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,1]]},"references-count":33,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2016.2620981","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,1]]}}}