{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T07:45:17Z","timestamp":1768463117703,"version":"3.49.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020"},{"name":"Marie Sklodowska-Curie","award":["813533"],"award-info":[{"award-number":["813533"]}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["342730_153158\/1"],"award-info":[{"award-number":["342730_153158\/1"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["320030_201060\/1"],"award-info":[{"award-number":["320030_201060\/1"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Swiss Society of Intensive Care, the Bangerter Foundation"},{"name":"Vinetum and Borer Foundation"},{"name":"Foundation for the Health of Children and Adolescents"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Sepsis is a leading cause of death and disability in children globally, accounting for \u223c3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) is considered a significant risk factor for adverse clinical outcomes characterized by high mortality and morbidity in the pediatric intensive care unit. The recent rapidly growing availability of electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning in healthcare and achieved great successes. However, effective machine learning models which could make the accurate early prediction of the recovery in pediatric sepsis patients from MODS to a mild state and thus assist the clinicians in the decision-making process is still lacking.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>This study develops a machine learning-based approach to predict the recovery from MODS to zero or single organ dysfunction by 1 week in advance in the Swiss Pediatric Sepsis Study cohort of children with blood-culture confirmed bacteremia. Our model achieves internal validation performance on the SPSS cohort with an area under the receiver operating characteristic (AUROC) of 79.1% and area under the precision-recall curve (AUPRC) of 73.6%, and it was also externally validated on another pediatric sepsis patients cohort collected in the USA, yielding an AUROC of 76.4% and AUPRC of 72.4%. These results indicate that our model has the potential to be included into the EHRs system and contribute to patient assessment and triage in pediatric sepsis patient care.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>Code available at https:\/\/github.com\/BorgwardtLab\/MODS-recovery. The data underlying this article is not publicly available for the privacy of individuals that participated in the study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac229","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"i101-i108","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of recovery from multiple organ dysfunction syndrome in pediatric sepsis patients"],"prefix":"10.1093","volume":"38","author":[{"given":"Bowen","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , Basel 4058, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , Lausanne 1015, Switzerland"}]},{"given":"Juliane","family":"Klatt","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , Basel 4058, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , Lausanne 1015, Switzerland"}]},{"given":"Michael M","family":"Moor","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , Basel 4058, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , Lausanne 1015, Switzerland"}]},{"given":"Latasha A","family":"Daniels","sequence":"additional","affiliation":[{"name":"Division of Critical Care, Ann and Robert H. Lurie Children\u2019s Hospital of Chicago , Chicago, IL, USA"}]},{"name":"Swiss Pediatric Sepsis Study","sequence":"additional","affiliation":[]},{"given":"Philipp K A","family":"Agyeman","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Berger","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Giannoni","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Stocker","sequence":"additional","affiliation":[]},{"given":"Klara M","family":"Posfay-Barbe","sequence":"additional","affiliation":[]},{"given":"Ulrich","family":"Heininger","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Bernhard-Stirnemann","sequence":"additional","affiliation":[]},{"given":"Anita","family":"Niederer-Loher","sequence":"additional","affiliation":[]},{"given":"Christian R","family":"Kahlert","sequence":"additional","affiliation":[]},{"given":"Giancarlo","family":"Natalucci","sequence":"additional","affiliation":[]},{"given":"Christa","family":"Relly","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Riedel","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Aebi","sequence":"additional","affiliation":[]},{"given":"Luregn J","family":"Schlapbach","sequence":"additional","affiliation":[]},{"given":"Lazaro N","family":"Sanchez-Pinto","sequence":"additional","affiliation":[{"name":"Division of Critical Care, Ann and Robert H. Lurie Children\u2019s Hospital of Chicago , Chicago, IL, USA"}]},{"given":"Philipp K A","family":"Agyeman","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Inselspital, Bern University Hospital University of Bern , Bern 3010, Switzerland"}]},{"given":"Luregn J","family":"Schlapbach","sequence":"additional","affiliation":[{"name":"Department of Intensive Care and Neonatology, and Children\u2019s Research Center, University Children\u2019s Hospital Zurich , Zurich 8032, Switzerland"},{"name":"Paediatric Intensive Care Unit, Child Health Research Center, Queensland Children\u2019s Hospital, The University of Queensland , Brisbane, Australia"}]},{"given":"Karsten M","family":"Borgwardt","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich , Basel 4058, Switzerland"},{"name":"SIB Swiss Institute of Bioinformatics , Lausanne 1015, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/S2352-4642(17)30010-X","article-title":"Epidemiology of blood culture-proven bacterial sepsis in children in Switzerland: a population-based cohort study","volume":"1","author":"Agyeman","year":"2017","journal-title":"Lancet. Child Adolesc. Health"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1146\/annurev-biodatasci-080917-013315","article-title":"Advances in electronic phenotyping: from rule-based definitions to machine learning models","volume":"1","author":"Banda","year":"2018","journal-title":"Annu. Rev. Biomed. Data Sci"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"805","DOI":"10.3389\/fped.2021.711104","article-title":"Early prediction of multiple organ dysfunction in the pediatric intensive care unit","volume":"9","author":"Bose","year":"2021","journal-title":"Front. Pediatr"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1055\/s-0038-1676634","article-title":"Epidemiology of pediatric septic shock","volume":"8","author":"de Souza","year":"2019","journal-title":"J. Pediatr. Intensive Care"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-0874-0","article-title":"On the interpretability of machine learning-based model for predicting hypertension","volume":"19","author":"Elshawi","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2431-14-199","article-title":"Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation","volume":"14","author":"Feudtner","year":"2014","journal-title":"BMC Pediatr"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e0192360","DOI":"10.1371\/journal.pone.0192360","article-title":"Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives","volume":"13","author":"Gehrmann","year":"2018","journal-title":"PLoS One"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.jpeds.2018.05.048","article-title":"Neonatal sepsis of early onset, and hospital-acquired and community-acquired late onset: a prospective population-based cohort study","volume":"201","author":"Giannoni","year":"2018","journal-title":"J. Pediatr"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1097\/01.PCC.0000149131.72248.E6","article-title":"International pediatric sepsis consensus conference: definitions for sepsis and organ dysfunction in pediatrics","volume":"6","author":"Goldstein","year":"2005","journal-title":"Pediatr. Crit. Care Med"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1038\/s41591-018-0213-5","article-title":"The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care","volume":"24","author":"Komorowski","year":"2018","journal-title":"Nat. Med"},{"key":"2023041407531310400_","first-page":"3146","article-title":"LightGBM: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inform. Process. Syst"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/PCC.0000000000000978","article-title":"New or progressive multiple organ dysfunction syndrome (NPMODs) in pediatric severe sepsis: a sepsis phenotype with higher morbidity and mortality","volume":"18","author":"Lin","year":"2017","journal-title":"Pediatr. Crit. Care Med"},{"key":"2023041407531310400_","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1097\/INF.0b013e3181d73322","article-title":"Continuing impact of infectious diseases on childhood deaths in England and Wales, 2003\u20132005","volume":"29","author":"Ladhani","year":"2010","journal-title":"Pediatr. Infect. Dis. J"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e355","DOI":"10.1097\/INF.0b013e3182930694","article-title":"Changes in infectious disease mortality in children during the past three decades","volume":"32","author":"Lantto","year":"2013","journal-title":"Pediatr. Infect. Dis. J"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1097\/CCM.0b013e31828a2bbd","article-title":"Pelod-2: an update of the pediatric logistic organ dysfunction score","volume":"41","author":"Leteurtre","year":"2013","journal-title":"Crit. Care Med"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e172352","DOI":"10.1001\/jamapediatrics.2017.2352","article-title":"Adaptation and validation of a pediatric sequential organ failure assessment score and evaluation of the sepsis-3 definitions in critically ill children","volume":"171","author":"Matics","year":"2017","journal-title":"JAMA Pediatr"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/S2213-2600(18)30300-X","article-title":"Machine learning for real-time prediction of complications in critical care: a retrospective study","volume":"6","author":"Meyer","year":"2018","journal-title":"Lancet Respir. Med"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"348","DOI":"10.3389\/fmed.2021.607952","article-title":"Early prediction of sepsis in the ICU using machine learning: a systematic review","volume":"8","author":"Moor","year":"2021","journal-title":"Front. Med"},{"key":"2023041407531310400_","article-title":"Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning","author":"Moor","year":"2021"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990\u20132017: analysis for the global burden of disease study","volume":"395","author":"Rudd","year":"2020","journal-title":"Lancet"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e209271","DOI":"10.1001\/jamanetworkopen.2020.9271","article-title":"Derivation and validation of novel phenotypes of multiple organ dysfunction syndrome in critically ill children","volume":"3","author":"Sanchez-Pinto","year":"2020","journal-title":"JAMA Netw. Open"},{"issue":"Suppl. 1","key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"S23","DOI":"10.1542\/peds.2021-052888D","article-title":"Scoring systems for organ dysfunction and multiple organ dysfunction: the podium consensus conference","volume":"149","author":"Schlapbach","year":"2022","journal-title":"Pediatrics"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e348","DOI":"10.1542\/peds.2010-3338","article-title":"Impact of sepsis on neurodevelopmental outcome in a Swiss National Cohort of extremely premature infants","volume":"128","author":"Schlapbach","year":"2011","journal-title":"Pediatrics"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"e1379","DOI":"10.1002\/widm.1379","article-title":"Interpretability of machine learning-based prediction models in healthcare","volume":"10","author":"Stiglic","year":"2020","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"S13","DOI":"10.1542\/peds.2021-052888C","article-title":"Refining the pediatric multiple organ dysfunction syndrome","volume":"149","author":"Weiss","year":"2022","journal-title":"Pediatrics"},{"key":"2023041407531310400_","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","article-title":"External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients","volume":"181","author":"Wong","year":"2021","journal-title":"JAMA Intern. Med"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/Supplement_1\/i101\/49886735\/btac229.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/Supplement_1\/i101\/49886735\/btac229.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:28:31Z","timestamp":1700440111000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/Supplement_1\/i101\/6617486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,24]]},"references-count":29,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2022,6,24]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac229","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,1]]},"published":{"date-parts":[[2022,6,24]]}}}