{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T14:26:22Z","timestamp":1794407182333,"version":"4.9.0"},"reference-count":32,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Heliyon"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1016\/j.heliyon.2023.e20693","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T02:25:11Z","timestamp":1696472711000},"page":"e20693","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"title":["Prediction of neonatal death in pregnant women in an intensive care unit: Application of machine learning models"],"prefix":"10.1016","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1005-5158","authenticated-orcid":false,"given":"Marcos","family":"Espinola-S\u00e1nchez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0517-2114","authenticated-orcid":false,"given":"Silvia","family":"Sanca-Valeriano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6055-0416","authenticated-orcid":false,"given":"Andres","family":"Campa\u00f1a-Acu\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Caballero-Alvarado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.heliyon.2023.e20693_bib1","series-title":"Levels and Trends in Child Mortality: Report","year":"2020"},{"key":"10.1016\/j.heliyon.2023.e20693_bib2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/1471-2393-13-18","article-title":"The effect of health facility delivery on neonatal mortality: systematic review and meta-analysis","volume":"13","author":"Tura","year":"2013","journal-title":"BMC Pregnancy Childbirth"},{"key":"10.1016\/j.heliyon.2023.e20693_bib4","series-title":"Newborn Mortality","year":"2022"},{"key":"10.1016\/j.heliyon.2023.e20693_bib5","series-title":"Peru. Infant and Childhood Mortality","year":"2018"},{"issue":"Suppl 1","key":"10.1016\/j.heliyon.2023.e20693_bib6","doi-asserted-by":"crossref","first-page":"S4","DOI":"10.1016\/j.gaceta.2022.02.008","article-title":"Health impact of COVID pandemic. SESPAS Report 2022","volume":"36","author":"Torres-Cantero","year":"2022","journal-title":"Gac. Sanit."},{"issue":"3","key":"10.1016\/j.heliyon.2023.e20693_bib7","first-page":"281","article-title":"Neonatal mortality: a challenge for the country and the university","volume":"80","author":"C\u00e1rdenas","year":"2019","journal-title":"An. Fac. Med."},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib8","first-page":"66","article-title":"Evolution of neonatal mortality rate in the amazonas region, Peru","volume":"3","author":"Choton","year":"2020","journal-title":"UNTRM Scientific Journal: Social Sciences and Humanities"},{"key":"10.1016\/j.heliyon.2023.e20693_bib9","first-page":"87","article-title":"Risk factors of neonatal mortality in Iran: a systematic review","volume":"33","author":"Daemi","year":"2019","journal-title":"Med. J. Islam. Repub. Iran"},{"issue":"4","key":"10.1016\/j.heliyon.2023.e20693_bib10","doi-asserted-by":"crossref","first-page":"424","DOI":"10.4067\/s0717-75262017000400424","article-title":"Biological determinants of neonatal mortality in a population of adolescent and adult women at a hospital in Colombia","volume":"82","author":"Mendoza","year":"2017","journal-title":"Rev. Chil. Obstet. Ginecolog."},{"issue":"4","key":"10.1016\/j.heliyon.2023.e20693_bib11","doi-asserted-by":"crossref","first-page":"375","DOI":"10.3329\/jhpn.v28i4.6044","article-title":"Causes of neonatal deaths in a rural subdistrict of Bangladesh: implications for intervention","volume":"28","author":"Chowdhury","year":"2010","journal-title":"J. Health Popul. Nutr."},{"issue":"3","key":"10.1016\/j.heliyon.2023.e20693_bib12","first-page":"213","article-title":"Development and validation of the Neonatal Mortality Score-9 Mexico to predict mortality in critically ill neonates","volume":"113","author":"M\u00e1rquez-Gonz\u00e1lez","year":"2015","journal-title":"Arch. Argent. Pediatr."},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib13","first-page":"1","article-title":"Proposal of a novel predictive model for mortality in high-risk neonates and evaluation of its performance","volume":"22","author":"Noboa Salgado","year":"2021","journal-title":"Rev. Ecuat. Pediatr."},{"key":"10.1016\/j.heliyon.2023.e20693_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105639","article-title":"NeoAI 1.0: machine learning-based paradigm for prediction of neonatal and infant risk of death","volume":"147","author":"Teji","year":"2022","journal-title":"Comput. Biol. Med."},{"issue":"2","key":"10.1016\/j.heliyon.2023.e20693_bib15","doi-asserted-by":"crossref","first-page":"248","DOI":"10.4067\/s0034-98872021000200248","article-title":"Overview of machine learning and its application in the management of emergency services","volume":"149","author":"Pedrero","year":"2021","journal-title":"Rev. Med. Chile"},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.anpedi.2019.11.003","article-title":"Prediction of mortality in premature neonates. An updated systematic review","volume":"93","author":"Del R\u00edo","year":"2020","journal-title":"An. Pediatr."},{"issue":"2","key":"10.1016\/j.heliyon.2023.e20693_bib17","doi-asserted-by":"crossref","first-page":"110","DOI":"10.29262\/ram.v61i2.33","article-title":"Artificial intelligence to assist clinical diagnosis in medicine","volume":"61","author":"Lugo-Reyes","year":"2014","journal-title":"Rev. Alerg. Mex."},{"issue":"12","key":"10.1016\/j.heliyon.2023.e20693_bib18","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1016\/j.recesp.2019.05.016","article-title":"Applications of artificial intelligence in cardiology. The future is already here","volume":"72","author":"Dorado-D\u00edaz","year":"2019","journal-title":"Rev. Esp. Cardiol."},{"issue":"3","key":"10.1016\/j.heliyon.2023.e20693_bib19","doi-asserted-by":"crossref","first-page":"554","DOI":"10.17843\/rpmesp.2020.373.5585","article-title":"Artificial intelligence and innovation to optimize the tuberculosis diagnostic process","volume":"37","author":"Curioso","year":"2020","journal-title":"Rev. Peru. Med. Exp. Salud P\u00fablica"},{"key":"10.1016\/j.heliyon.2023.e20693_bib20","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s12652-020-02562-2","article-title":"Prediction of mortality of premature neonates using neural network and logistic regression","volume":"13","author":"Rezaeian","year":"2020","journal-title":"J. Ambient Intell. Hum. Comput."},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib21","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1093\/ije\/dyy194","article-title":"A prediction model for neonatal mortality in low- and middle-income countries: an analysis of data from population surveillance sites in India, Nepal and Bangladesh","volume":"48","author":"Houweling","year":"2019","journal-title":"Int. J. Epidemiol."},{"issue":"10","key":"10.1016\/j.heliyon.2023.e20693_bib22","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2020-040132","article-title":"Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania","volume":"10","author":"Mboya","year":"2020","journal-title":"BMJ Open"},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib23","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1186\/s12884-022-04699-8","article-title":"Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014-15 dataset","volume":"22","author":"Mfateneza","year":"2022","journal-title":"BMC Pregnancy Childbirth"},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s41747-018-0061-6","article-title":"Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine","volume":"2","author":"Pesapane","year":"2018","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.heliyon.2023.e20693_bib25","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyg.2020.621251","article-title":"Sample size requirements for applying diagnostic classification models","volume":"11","author":"Sen","year":"2021","journal-title":"Front. Psychol."},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib26","first-page":"4","article-title":"Morbidity and maternal and fetal mortality in patients with severe preeclampsia","volume":"54","author":"Rubio","year":"2011","journal-title":"Progresos Obstet. Ginecol."},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1186\/s12911-021-01497-8","article-title":"Prediction of neonatal deaths in NICUs: development and validation of machine learning models","volume":"21","author":"Sheikhtaheri","year":"2021","journal-title":"BMC Med. Inf. Decis. Making"},{"issue":"1","key":"10.1016\/j.heliyon.2023.e20693_bib28","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12178-020-09600-8","article-title":"Machine learning and artificial intelligence: definitions, applications, and future directions","volume":"13","author":"Helm","year":"2020","journal-title":"Curr Rev Musculoskelet Med"},{"issue":"7","key":"10.1016\/j.heliyon.2023.e20693_bib29","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TNNLS.2020.3009776","article-title":"GBDT-MO: gradient-boosted decision trees for multiple outputs","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE Transact. Neural Networks Learn. Syst."},{"key":"10.1016\/j.heliyon.2023.e20693_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106993","article-title":"Interpretable machine learning with an Ensemble of gradient boosting machines","volume":"222","author":"Konstantinov","year":"2021","journal-title":"Knowl. Base Syst."},{"issue":"11","key":"10.1016\/j.heliyon.2023.e20693_bib31","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.26750","article-title":"Predictive modeling for perinatal mortality in resource-limited settings","volume":"3","author":"Shukla","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.heliyon.2023.e20693_bib32","doi-asserted-by":"crossref","first-page":"123347","DOI":"10.1109\/ACCESS.2020.3006710","article-title":"Machine learning methods for neonatal mortality and morbidity classification","volume":"8","author":"Jaskari","year":"2020","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.heliyon.2023.e20693_bib33","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1159\/000516891","article-title":"Machine learning models for predicting neonatal mortality: a systematic review","volume":"118","author":"Mangold","year":"2021","journal-title":"Neonatology"}],"container-title":["Heliyon"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S240584402307901X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S240584402307901X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T08:31:01Z","timestamp":1790757061000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S240584402307901X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":32,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["S240584402307901X"],"URL":"https:\/\/doi.org\/10.1016\/j.heliyon.2023.e20693","relation":{},"ISSN":["2405-8440"],"issn-type":[{"value":"2405-8440","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of neonatal death in pregnant women in an intensive care unit: Application of machine learning models","name":"articletitle","label":"Article Title"},{"value":"Heliyon","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.heliyon.2023.e20693","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"e20693"}}