{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:25Z","timestamp":1747216165178,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"print","value":"9781643684567"},{"type":"electronic","value":"9781643684574"}],"license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,25]]},"abstract":"<jats:p>This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)\/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR\/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model\u2019s usage impacts patient outcomes.<\/jats:p>","DOI":"10.3233\/shti231063","type":"book-chapter","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:00Z","timestamp":1706178300000},"source":"Crossref","is-referenced-by-count":1,"title":["Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9178-0222","authenticated-orcid":false,"given":"Herdiantri","family":"Sufriyana","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan"},{"name":"Department of Medical Physiology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8887-0304","authenticated-orcid":false,"given":"Fariska Zata","family":"Amani","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2302-1640","authenticated-orcid":false,"given":"Aufar Zimamuz Zaman","family":"Al Hajiri","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-1194","authenticated-orcid":false,"given":"Yu-Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan"},{"name":"Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4801-5159","authenticated-orcid":false,"given":"Emily Chia-Yu","family":"Su","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan"},{"name":"Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan"},{"name":"Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2023 \u2014 The Future Is Accessible"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI231063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T10:25:01Z","timestamp":1706178301000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI231063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"ISBN":["9781643684567","9781643684574"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti231063","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}