{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T03:20:32Z","timestamp":1789442432544,"version":"build-2803163510"},"reference-count":52,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","sfnmjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Seminars in Fetal and Neonatal Medicine"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.siny.2025.101687","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T14:16:46Z","timestamp":1763648206000},"page":"101687","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence in neonatal sepsis: Scope, challenges, and potential solutions!"],"prefix":"10.1016","volume":"31","author":[{"given":"Deepika","family":"Kainth","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramesh","family":"Agarwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.siny.2025.101687_bib1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/S2213-2600(18)30063-8","article-title":"The global burden of paediatric and neonatal sepsis: a systematic review","volume":"6","author":"Fleischmann-Struzek","year":"2018","journal-title":"Lancet Respir Med"},{"key":"10.1016\/j.siny.2025.101687_bib2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3389\/fped.2018.00285","article-title":"Culture-negative early-onset neonatal sepsis - at the crossroad between efficient sepsis care and antimicrobial stewardship","volume":"6","author":"Klingenberg","year":"2018","journal-title":"Front Pediatr"},{"issue":"10","key":"10.1016\/j.siny.2025.101687_bib3","doi-asserted-by":"crossref","first-page":"e752","DOI":"10.1016\/S2214-109X(16)30148-6","article-title":"Characterisation and antimicrobial resistance of sepsis pathogens in neonates born in tertiary care centres in Delhi, India: a cohort study","volume":"4","year":"2016","journal-title":"Lancet Global Health"},{"issue":"10142","key":"10.1016\/j.siny.2025.101687_bib5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/S0140-6736(18)31432-6","article-title":"Improving management of neonatal infections","volume":"392","author":"Seale","year":"2018","journal-title":"Lancet"},{"issue":"1","key":"10.1016\/j.siny.2025.101687_bib6","doi-asserted-by":"crossref","first-page":"F25","DOI":"10.1136\/fn.87.1.F25","article-title":"Blood cultures in newborns and children: optimising an everyday test","volume":"87","author":"Buttery","year":"2002","journal-title":"Arch Dis Child Fetal Neonatal Ed"},{"issue":"7","key":"10.1016\/j.siny.2025.101687_bib7","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1038\/s41372-019-0363-4","article-title":"Procalcitonin versus C-reactive protein: review of kinetics and performance for diagnosis of neonatal sepsis","volume":"39","author":"Eschborn","year":"2019","journal-title":"J Perinatol"},{"issue":"3","key":"10.1016\/j.siny.2025.101687_bib8","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1001\/jamapediatrics.2019.5669","article-title":"Assessment of C-Reactive protein diagnostic test accuracy for late-onset infection in newborn infants: a systematic review and meta-analysis","volume":"174","author":"Brown","year":"2020","journal-title":"JAMA Pediatr"},{"issue":"5","key":"10.1016\/j.siny.2025.101687_bib9","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1159\/000529640","article-title":"Procalcitonin for detecting culture-positive sepsis in neonates: a prospective, multicenter study","volume":"120","author":"Chaurasia","year":"2023","journal-title":"Neonatology"},{"key":"10.1016\/j.siny.2025.101687_bib10","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1186\/s13054-018-2236-1","article-title":"The combination of procalcitonin and C-reactive protein or presepsin alone improves the accuracy of diagnosis of neonatal sepsis: a meta-analysis and systematic review","volume":"22","author":"Ruan","year":"2018","journal-title":"Crit Care"},{"issue":"5","key":"10.1016\/j.siny.2025.101687_bib11","doi-asserted-by":"crossref","DOI":"10.1542\/peds.2019-2576","article-title":"The spartacus problem: diagnostic inefficiency of neonatal sepsis","volume":"144","author":"Cantey","year":"2019","journal-title":"Pediatrics"},{"issue":"5","key":"10.1016\/j.siny.2025.101687_bib12","doi-asserted-by":"crossref","first-page":"e1155","DOI":"10.1542\/peds.2010-3464","article-title":"Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors","volume":"128","author":"Puopolo","year":"2011","journal-title":"Pediatrics"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib13","doi-asserted-by":"crossref","first-page":"F99","DOI":"10.1136\/adc.2009.163220","article-title":"Intrapartum antibiotics and risk factors for early onset sepsis","volume":"95","author":"Dutta","year":"2010","journal-title":"Arch Dis Child Fetal Neonatal Ed"},{"issue":"3","key":"10.1016\/j.siny.2025.101687_bib14","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1111\/j.1651-2227.1979.tb05016.x","article-title":"Neonatal septicemia and perinatal risk factors","volume":"68","author":"Bergqvist","year":"1979","journal-title":"Acta Paediatr Scand"},{"issue":"6","key":"10.1016\/j.siny.2025.101687_bib15","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1016\/j.jpeds.2011.06.044","article-title":"Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial","volume":"159","author":"Moorman","year":"2011","journal-title":"J Pediatr"},{"key":"10.1016\/j.siny.2025.101687_bib16","unstructured":"(78) machine learning explained: understanding supervised, unsupervised, and reinforcement learning | LinkedIn [Internet]. [cited 2025 Jun 15]. Available from: https:\/\/www.linkedin.com\/pulse\/machine-learning-explained-understanding-supervised-ronald-van-loon\/."},{"issue":"3","key":"10.1016\/j.siny.2025.101687_bib17","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s00167-022-06896-6","article-title":"Machine learning and conventional statistics: making sense of the differences","volume":"30","author":"Ley","year":"2022","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib18","doi-asserted-by":"crossref","first-page":"e383","DOI":"10.1093\/cid\/ciaa876","article-title":"C-Reactive protein, procalcitonin, and white blood count to rule out neonatal early-onset sepsis within 36 hours: a secondary analysis of the neonatal procalcitonin intervention study","volume":"73","author":"Stocker","year":"2021","journal-title":"Clin Infect Dis"},{"key":"10.1016\/j.siny.2025.101687_bib19","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"issue":"9","key":"10.1016\/j.siny.2025.101687_bib20","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1097\/INF.0000000000004409","article-title":"Diagnostic performance of machine learning-based models in neonatal sepsis: a systematic review","volume":"43","author":"Kainth","year":"2024","journal-title":"Pediatr Infect Dis J"},{"issue":"1","key":"10.1016\/j.siny.2025.101687_bib21","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1542\/peds.2013-1689","article-title":"Stratification of risk of early-onset sepsis in newborns \u2265 34 weeks' gestation","volume":"133","author":"Escobar","year":"2014","journal-title":"Pediatrics"},{"issue":"1","key":"10.1016\/j.siny.2025.101687_bib22","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1159\/000497781","article-title":"Risk factors for late-onset sepsis in preterm infants: a multicenter case-control study","volume":"116","author":"El","year":"2019","journal-title":"Neonatology"},{"issue":"4","key":"10.1016\/j.siny.2025.101687_bib23","doi-asserted-by":"crossref","first-page":"3644","DOI":"10.3390\/ijerph20043644","article-title":"Tackling neonatal sepsis-can it be predicted?","volume":"20","author":"But","year":"2023","journal-title":"Int J Environ Res Publ Health"},{"issue":"12","key":"10.1016\/j.siny.2025.101687_bib24","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3390\/children4120111","article-title":"Performance comparison of systemic inflammatory response syndrome with logistic regression models to predict sepsis in neonates","volume":"4","author":"Thakur","year":"2017","journal-title":"Children"},{"key":"10.1016\/j.siny.2025.101687_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.clinsp.2022.100148","article-title":"Complete blood count and C-reactive protein to predict positive blood culture among neonates using machine learning algorithms","author":"Matsushita","year":"2023","journal-title":"Clinics"},{"issue":"12","key":"10.1016\/j.siny.2025.101687_bib26","doi-asserted-by":"crossref","first-page":"3201","DOI":"10.1111\/apa.16083","article-title":"Neonatal sepsis prediction through clinical decision support algorithms: a systematic review","volume":"110","author":"Persad","year":"2021","journal-title":"Acta Paediatr Int J Paediatr"},{"key":"10.1016\/j.siny.2025.101687_bib27","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3389\/fped.2020.00525","article-title":"Neonatal sepsis diagnosis decision-making based on artificial neural networks","volume":"8","author":"Helguera-Repetto","year":"2020","journal-title":"Front Pediatr"},{"key":"10.1016\/j.siny.2025.101687_bib28","doi-asserted-by":"crossref","first-page":"131","DOI":"10.12688\/wellcomeopenres.14847.1","article-title":"Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia","volume":"3","author":"Oonsivilai","year":"2018","journal-title":"Wellcome Open Res"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib29","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ccm.2016.01.004","article-title":"Development and implementation of sepsis alert systems","volume":"37","author":"Harrison","year":"2016","journal-title":"Clin Chest Med"},{"issue":"7","key":"10.1016\/j.siny.2025.101687_bib30","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.3390\/biomedicines11071853","article-title":"Bioinformatic analysis and machine learning methods in neonatal sepsis: identification of biomarkers and immune infiltration","volume":"11","author":"Jiang","year":"2023","journal-title":"Biomedicines"},{"issue":"4","key":"10.1016\/j.siny.2025.101687_bib31","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1055\/s-0041-1728829","article-title":"Sepsis and mortality prediction in very low birth weight infants: analysis of HeRO and nSOFA","volume":"40","author":"Zeigler","year":"2023","journal-title":"Am J Perinatol"},{"issue":"7","key":"10.1016\/j.siny.2025.101687_bib32","doi-asserted-by":"crossref","DOI":"10.2196\/15965","article-title":"A predictive model based on machine learning for the early detection of late-onset neonatal sepsis: development and observational study","volume":"8","author":"Song","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"3","key":"10.1016\/j.siny.2025.101687_bib33","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/JBHI.2019.2927463","article-title":"Predicting neonatal sepsis using features of heart rate variability, respiratory characteristics, and ECG-derived estimates of infant motion","volume":"24","author":"Joshi","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib34","first-page":"226","article-title":"Deep learning approach on tabular data to predict early-onset neonatal sepsis","volume":"5","author":"Alvi","year":"2021","journal-title":"J Info Telecommun"},{"issue":"4","key":"10.1016\/j.siny.2025.101687_bib35","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.amjsurg.2018.07.041","article-title":"Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm","volume":"216","author":"Bartz-Kurycki","year":"2018","journal-title":"Am J Surg"},{"key":"10.1016\/j.siny.2025.101687_bib36","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.compeleceng.2019.04.015","article-title":"A neural network approach to predict early neonatal sepsis","volume":"76","author":"L\u00f3pez-Mart\u00ednez","year":"2019","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.siny.2025.101687_bib37","series-title":"2020 IEEE 10th international conference on intelligent systems (IS)","first-page":"443","article-title":"Predicting early neonatal sepsis using neural networks and other classifiers","author":"Alvi","year":"2020"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib38","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1177\/0272989X09337791","article-title":"Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis","volume":"30","author":"Xiao","year":"2010","journal-title":"Med Decis Mak"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib39","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1136\/amiajnl-2013-001854","article-title":"Medical decision support using machine learning for early detection of late-onset neonatal sepsis","volume":"21","author":"Mani","year":"2014","journal-title":"J Am Med Inf Assoc"},{"issue":"5","key":"10.1016\/j.siny.2025.101687_bib40","doi-asserted-by":"crossref","first-page":"2498","DOI":"10.3390\/ijerph19052498","article-title":"Predictive analysis of healthcare-associated blood stream infections in the neonatal intensive care unit using artificial intelligence: a single center study","volume":"19","author":"Montella","year":"2022","journal-title":"Int J Environ Res Publ Health"},{"issue":"4","key":"10.1016\/j.siny.2025.101687_bib41","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1111\/apa.16660","article-title":"Vital sign-based detection of sepsis in neonates using machine learning","volume":"112","author":"Honor\u00e9","year":"2023","journal-title":"Acta Paediatr"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib42","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0212665","article-title":"Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data","volume":"14","author":"Masino","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.siny.2025.101687_bib43","article-title":"Development and validation of clinical prediction models for acute kidney injury recovery at hospital discharge in critically ill adults","author":"Huang","year":"2022","journal-title":"J Clin Monit Comput"},{"key":"10.1016\/j.siny.2025.101687_bib44","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/3567194","article-title":"Neonatal disease prediction using machine learning techniques","volume":"2023","author":"Robi","year":"2023","journal-title":"J Healthc Eng"},{"key":"10.1016\/j.siny.2025.101687_bib45","doi-asserted-by":"crossref","DOI":"10.3389\/fdgth.2022.862095","article-title":"Erroneous data: the achilles' heel of AI and personalized medicine","volume":"4","author":"Kristiansen","year":"2022","journal-title":"Front Digit Health"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib46","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1136\/archdischild-2012-302033","article-title":"Community-acquired neonatal and infant sepsis in developing countries: efficacy of WHO's currently recommended antibiotics--systematic review and meta-analysis","volume":"98","author":"Downie","year":"2013","journal-title":"Arch Dis Child"},{"issue":"2 Pt 1","key":"10.1016\/j.siny.2025.101687_bib47","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1542\/peds.110.2.285","article-title":"Late-onset sepsis in very low birth weight neonates: the experience of the NICHD neonatal research network","volume":"110","author":"Stoll","year":"2002","journal-title":"Pediatrics"},{"issue":"9465","key":"10.1016\/j.siny.2025.101687_bib48","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1016\/S0140-6736(05)71881-X","article-title":"Hospital-acquired neonatal infections in developing countries","volume":"365","author":"Zaidi","year":"2005","journal-title":"Lancet"},{"issue":"2","key":"10.1016\/j.siny.2025.101687_bib49","first-page":"273","article-title":"Advancing continuous predictive analytics monitoring: moving from implementation to clinical action in a learning health system","volume":"30","author":"Keim-Malpass","year":"2018","journal-title":"Crit Care Nurs Clin"},{"issue":"1","key":"10.1016\/j.siny.2025.101687_bib50","article-title":"Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare","volume":"12","author":"Goh","year":"2021","journal-title":"Nat Commun [Internet]"},{"issue":"11","key":"10.1016\/j.siny.2025.101687_bib51","doi-asserted-by":"crossref","first-page":"E1091","DOI":"10.1097\/CCM.0000000000004550","article-title":"An explainable artificial intelligence predictor for early detection of sepsis","volume":"48","author":"Yang","year":"2020","journal-title":"Crit Care Med"},{"issue":"1","key":"10.1016\/j.siny.2025.101687_bib52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-025-01541-1","article-title":"Ethical considerations in AI for child health and recommendations for child-centered medical AI","volume":"8","author":"Chng","year":"2025","journal-title":"npj Digit Med"},{"key":"10.1016\/j.siny.2025.101687_bib53","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jss.2023.10.027","article-title":"Attitudes on artificial intelligence use in pediatric care from parents of hospitalized children","volume":"295","author":"Haley","year":"2024","journal-title":"J Surg Res"}],"container-title":["Seminars in Fetal and Neonatal Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1744165X25000812?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1744165X25000812?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:36:36Z","timestamp":1770698196000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1744165X25000812"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":52,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["S1744165X25000812"],"URL":"https:\/\/doi.org\/10.1016\/j.siny.2025.101687","relation":{},"ISSN":["1744-165X"],"issn-type":[{"value":"1744-165X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in neonatal sepsis: Scope, challenges, and potential solutions!","name":"articletitle","label":"Article Title"},{"value":"Seminars in Fetal and Neonatal Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.siny.2025.101687","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"101687"}}