A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and va...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PloS one Jg. 9; H. 2; S. e89860
Hauptverfasser:	Ji, Jun, Ling, Xuefeng B., Zhao, Yingzhen, Hu, Zhongkai, Zheng, Xiaolin, Xu, Zhening, Wen, Qiaojun, Kastenberg, Zachary J., Li, Ping, Abdullah, Fizan, Brandt, Mary L., Ehrenkranz, Richard A., Harris, Mary Catherine, Lee, Timothy C., Simpson, B. Joyce, Bowers, Corinna, Moss, R. Lawrence, Sylvester, Karl G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 28.02.2014 Public Library of Science (PLoS)
Schlagworte:	Algorithms Biology Birth weight Colleges & universities Computer science Consortia Data processing Development and progression Diagnosis Diagnostic systems Discriminant analysis Disease Enterocolitis Enterocolitis, Necrotizing - diagnosis Enterocolitis, Necrotizing - pathology Epidemiology Female Gangrene Gastrointestinal diseases Health aspects Health risks Hospitals Humans Infant, Newborn Infants Laboratories Learning algorithms Machine learning Male Medical diagnosis Medical prognosis Medicine Morbidity Mortality Necrosis Necrotizing enterocolitis Neonates Newborn babies Pediatrics Process controls Prognosis Risk Smartphones Surgery Training Urine Ohio California United States > US Texas China
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.
AbstractList	Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. Study design A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Results Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. Algorithm availability Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. Study design A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Results Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. Algorithm availability http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request. Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request. Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. Study design A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Results Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. Algorithm availability http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request. Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. BackgroundNecrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.Study designA six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.ResultsMachine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.Algorithm availabilityhttp://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request. Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.BACKGROUNDNecrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.STUDY DESIGNA six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.RESULTSMachine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.ALGORITHM AVAILABILITYhttp://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.
Audience	Academic
Author	Abdullah, Fizan Ji, Jun Li, Ping Sylvester, Karl G. Ling, Xuefeng B. Wen, Qiaojun Moss, R. Lawrence Lee, Timothy C. Kastenberg, Zachary J. Zheng, Xiaolin Simpson, B. Joyce Ehrenkranz, Richard A. Bowers, Corinna Xu, Zhening Hu, Zhongkai Zhao, Yingzhen Harris, Mary Catherine Brandt, Mary L.
AuthorAffiliation	4 College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China 3 Department of Surgery, Stanford University, Stanford, California, United States of America 5 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America 10 Department of Surgery, Ohio State College of Medicine, Columbus, Ohio, United States of America 7 Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America 2 School of Health Management, Hangzhou Normal University, Hangzhou, Zhejiang, China Emory University School of Medicine, United States of America 9 Division of Pediatric Surgery, Nationwide Children’s Hospital, Columbus, Ohio, United States of America 6 Department of Surgery, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas, United States of America 8 Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America 1 St
AuthorAffiliation_xml	– name: 6 Department of Surgery, Texas Children’s Hospital, Baylor College of Medicine, Houston, Texas, United States of America – name: Emory University School of Medicine, United States of America – name: 3 Department of Surgery, Stanford University, Stanford, California, United States of America – name: 7 Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut, United States of America – name: 9 Division of Pediatric Surgery, Nationwide Children’s Hospital, Columbus, Ohio, United States of America – name: 10 Department of Surgery, Ohio State College of Medicine, Columbus, Ohio, United States of America – name: 2 School of Health Management, Hangzhou Normal University, Hangzhou, Zhejiang, China – name: 8 Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America – name: 5 Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America – name: 1 State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou, Zhejiang, China – name: 4 College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
Author_xml	– sequence: 1 givenname: Jun surname: Ji fullname: Ji, Jun – sequence: 2 givenname: Xuefeng B. surname: Ling fullname: Ling, Xuefeng B. – sequence: 3 givenname: Yingzhen surname: Zhao fullname: Zhao, Yingzhen – sequence: 4 givenname: Zhongkai surname: Hu fullname: Hu, Zhongkai – sequence: 5 givenname: Xiaolin surname: Zheng fullname: Zheng, Xiaolin – sequence: 6 givenname: Zhening surname: Xu fullname: Xu, Zhening – sequence: 7 givenname: Qiaojun surname: Wen fullname: Wen, Qiaojun – sequence: 8 givenname: Zachary J. surname: Kastenberg fullname: Kastenberg, Zachary J. – sequence: 9 givenname: Ping surname: Li fullname: Li, Ping – sequence: 10 givenname: Fizan surname: Abdullah fullname: Abdullah, Fizan – sequence: 11 givenname: Mary L. surname: Brandt fullname: Brandt, Mary L. – sequence: 12 givenname: Richard A. surname: Ehrenkranz fullname: Ehrenkranz, Richard A. – sequence: 13 givenname: Mary Catherine surname: Harris fullname: Harris, Mary Catherine – sequence: 14 givenname: Timothy C. surname: Lee fullname: Lee, Timothy C. – sequence: 15 givenname: B. Joyce surname: Simpson fullname: Simpson, B. Joyce – sequence: 16 givenname: Corinna surname: Bowers fullname: Bowers, Corinna – sequence: 17 givenname: R. Lawrence surname: Moss fullname: Moss, R. Lawrence – sequence: 18 givenname: Karl G. surname: Sylvester fullname: Sylvester, Karl G.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24587080$$D View this record in MEDLINE/PubMed
BookMark	eNqNk02P0zAQhiO0iN0t_AMEkZAQHFrsOF_mgFS1u1CpYhFfV2viOKlXrl1sZ8Vy45_jtClqVyuEfEgyed53PKOZ8-hEGy2i6ClGE0wK_ObadFaDmmxCeIJQScscPYjOMCXJOE8QOTl4P43OnbtGKCNlnj-KTpM0KwtUorPo9zSeg4fx3MoboeOpao2VfrWOF9qL1oKXuo1nSmrJQcWg63gJlQlxY2_jSwG-s8LFjbGxX4l4LqHVxkm3JT9ZM3yZJv4ouDVe_ur9LoK3Ndwo6aV7HD1sQDnxZHiOom-XF19nH8bLq_eL2XQ55jlN_BhQXRWopkUtAEoEeUpRlfAGpw3GJSmzTBCRF4hSkYqG0AKnKG8IwQXmpKI5GUXPd74bZRwbuucYzhBJCkwTGojFjqgNXLONlWuwt8yAZNuAsS0D6yVXguVAE56TDEqK0owQyIBXuK6AZrwqQv5R9G7I1lVrUXOhvQV1ZHr8R8sVa80NIzTUgvrLvBoMrPnRCefZWjoulAItTLe9d4qzMt2iL-6g91c3UC2EAqRuTMjLe1M2TYuyPxQHanIPFU4t1pKHUWtkiB8JXh8JAuPFT99C5xxbfPn8_-zV92P25QG7EqD8yhnVeWm0OwafHXb6b4v3Mx6AtzsgDKBzVjSMSw-9TyhNKoYR6xdq3zTWLxQbFiqI0zvivf8_ZX8ABf4lgg
CitedBy_id	crossref_primary_10_1038_s41390_021_01570_y crossref_primary_10_1038_s41746_023_00941_5 crossref_primary_10_3390_children12040498 crossref_primary_10_1007_s00383_020_04819_5 crossref_primary_10_1038_s41467_020_18750_9 crossref_primary_10_1055_a_2253_8656 crossref_primary_10_1007_s00383_022_05095_1 crossref_primary_10_1038_s41372_023_01685_6 crossref_primary_10_1038_s41390_022_02181_x crossref_primary_10_1038_s41372_022_01347_z crossref_primary_10_1007_s00383_025_06185_6 crossref_primary_10_1186_s12859_022_04618_w crossref_primary_10_3390_ijms24032471 crossref_primary_10_1038_s41390_024_03074_x crossref_primary_10_1159_000458462 crossref_primary_10_1097_MIB_0000000000000184 crossref_primary_10_1038_srep13119 crossref_primary_10_3390_ijerph15112509 crossref_primary_10_1038_s41372_018_0245_1 crossref_primary_10_1038_s41390_020_1074_4 crossref_primary_10_1016_j_jpeds_2017_08_046 crossref_primary_10_1177_0973217920987662 crossref_primary_10_1038_s41390_024_03292_3 crossref_primary_10_12688_f1000research_6888_1 crossref_primary_10_1097_JPN_0000000000000082 crossref_primary_10_1038_s41372_020_0611_7 crossref_primary_10_1016_j_semperi_2022_151693 crossref_primary_10_1053_j_sempedsurg_2017_11_002 crossref_primary_10_1007_s00431_024_05505_7 crossref_primary_10_1016_j_arcmed_2022_11_002 crossref_primary_10_3389_fped_2022_1048322 crossref_primary_10_3389_fped_2023_1182597 crossref_primary_10_1007_s00383_023_05619_3
Cites_doi	10.1542/peds.2005-1273 10.1542/peds.107.1.e1 10.1038/jp.2008.119 10.1038/sj.jp.7210892 10.1016/j.jpeds.2013.10.091 10.18637/jss.v011.i10 10.1097/00000658-197801000-00001 10.1136/gutjnl-2013-305130 10.2215/CJN.00270113 10.1016/S0022-3468(87)80202-6 10.1016/S0022-3476(05)81124-2 10.1093/clinchem/39.4.561 10.1097/01.sla.0000164181.67862.7f 10.1093/bioinformatics/btp176 10.1542/peds.105.2.379 10.1542/peds.2004-0569
ContentType	Journal Article
Copyright	COPYRIGHT 2014 Public Library of Science 2014 Ji et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Ji et al 2014 Ji et al
Copyright_xml	– notice: COPYRIGHT 2014 Public Library of Science – notice: 2014 Ji et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2014 Ji et al 2014 Ji et al
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS PYCSY RC3 7X8 5PM DOA
DOI	10.1371/journal.pone.0089860
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection Biological Sciences Agricultural Science Database ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE Agricultural Science Database MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Sciences (General) Medicine Computer Science Biology
DocumentTitleAlternate	Pediatric NEC Diagnosis and Prognosis
EISSN	1932-6203
ExternalDocumentID	1503271929 oai_doaj_org_article_6a92c635a8904533a5acb1dba95cb7ef PMC3938509 3233513721 A478787891 24587080 10_1371_journal_pone_0089860
Genre	Research Support, Non-U.S. Gov't Journal Article
GeographicLocations	Ohio California United States--US Texas China
GeographicLocations_xml	– name: China – name: California – name: Texas – name: United States--US – name: Ohio
GrantInformation_xml	– fundername: NCATS NIH HHS grantid: UL1 TR001070
GroupedDBID	--- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACCTH ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV ADRAZ AEAQA AENEX AEUYN AFFHD AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PYCSY RNS RPM SV3 TR2 UKHRP WOQ WOW ~02 ~KM ALIPV BBORY CGR CUY CVF ECM EIF IPNFZ NPM PV9 RIG RZL 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO ESTFP FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI PRINS RC3 7X8 5PM AAPBV ABPTK N95
ID	FETCH-LOGICAL-c692t-a0db70d97deaa80a6490b2cf14f1183855e3e67099e4ef3971406f33171c3b963
IEDL.DBID	M7P
ISICitedReferencesCount	43
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000332396200089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-6203
IngestDate	Sun Aug 06 00:16:22 EDT 2023 Tue Oct 14 19:07:19 EDT 2025 Tue Nov 04 01:58:36 EST 2025 Sun Nov 09 12:33:55 EST 2025 Mon Oct 20 01:15:17 EDT 2025 Sat Nov 29 13:13:47 EST 2025 Sat Nov 29 10:13:45 EST 2025 Wed Nov 26 09:56:36 EST 2025 Wed Nov 26 09:51:11 EST 2025 Thu May 22 21:16:24 EDT 2025 Thu Apr 03 07:06:15 EDT 2025 Tue Nov 18 20:38:45 EST 2025 Sat Nov 29 04:04:10 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Creative Commons Attribution License
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c692t-a0db70d97deaa80a6490b2cf14f1183855e3e67099e4ef3971406f33171c3b963
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: XBL KGS JJ YZ. Performed the experiments: JJ XBL YZ. Analyzed the data: JJ XBL YZ. Contributed reagents/materials/analysis tools: YZ ZH XZ ZX ZJK JJ XBL. Wrote the paper: XBL JJ KGS ZJK QW PL. Interpretation of results: FA MLB RAE MCH TCL BJS CB RLM. Involved in critical revisions: JJ XBL YZ ZH XZ ZX QW ZJK PL FA MLB RAE MCH TCL BJS CB RLM KGS.
OpenAccessLink	https://www.proquest.com/docview/1503271929?pq-origsite=%requestingapplication%
PMID	24587080
PQID	1503271929
PQPubID	1436336
PageCount	e89860
ParticipantIDs	plos_journals_1503271929 doaj_primary_oai_doaj_org_article_6a92c635a8904533a5acb1dba95cb7ef pubmedcentral_primary_oai_pubmedcentral_nih_gov_3938509 proquest_miscellaneous_1504158409 proquest_journals_1503271929 gale_infotracmisc_A478787891 gale_infotracacademiconefile_A478787891 gale_incontextgauss_ISR_A478787891 gale_incontextgauss_IOV_A478787891 gale_healthsolutions_A478787891 pubmed_primary_24587080 crossref_citationtrail_10_1371_journal_pone_0089860 crossref_primary_10_1371_journal_pone_0089860
PublicationCentury	2000
PublicationDate	2014-02-28
PublicationDateYYYYMMDD	2014-02-28
PublicationDate_xml	– month: 02 year: 2014 text: 2014-02-28 day: 28
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Francisco – name: San Francisco, USA
PublicationTitle	PloS one
PublicationTitleAlternate	PLoS One
PublicationYear	2014
Publisher	Public Library of Science Public Library of Science (PLoS)
Publisher_xml	– name: Public Library of Science – name: Public Library of Science (PLoS)
References	ref13 RL Moss (ref16) 2008; 28 ref12 ref14 SO Guthrie (ref1) 2003; 23 ref20 WA Ballance (ref19) 1990; 117 ref22 (ref23); 25 ref10 ref21 ML Blakely (ref4) 2005; 241 SM Sutherland (ref24) 2013; 8 SR Hintz (ref6) 2005; 115 V Chongsuvivatwong (ref8) 2011; 2 MH Zweig (ref15) 1993; 39 MJ Bell (ref7) 1978; 187 MD Kamitsuka (ref2) 2000; 105 JA Lemons (ref3) 2001; 107 ref9 ML Blakely (ref5) 2006; 117 A Zeileis (ref11) 2002; 2 CJ McCormack (ref17) 1987; 22 MJ Bell (ref18) 1978; 187 16549503 - Pediatrics. 2006 Apr;117(4):e680-7 18784730 - J Perinatol. 2008 Oct;28(10):665-74 413500 - Ann Surg. 1978 Jan;187(1):1-7 24433829 - J Pediatr. 2014 Mar;164(3):607-12.e1-7 23833312 - Clin J Am Soc Nephrol. 2013 Oct;8(10):1661-9 24048736 - Gut. 2014 Aug;63(8):1284-92 2362230 - J Pediatr. 1990 Jul;117(1 Pt 2):S6-13 8472349 - Clin Chem. 1993 Apr;39(4):561-77 15741374 - Pediatrics. 2005 Mar;115(3):696-703 11134465 - Pediatrics. 2001 Jan;107(1):E1 19376824 - Bioinformatics. 2009 Jun 1;25(11):1461-2 12774133 - J Perinatol. 2003 Jun;23(4):278-85 10654959 - Pediatrics. 2000 Feb;105(2):379-84 3112356 - J Pediatr Surg. 1987 Jun;22(6):488-91 15912048 - Ann Surg. 2005 Jun;241(6):984-9; discussion 989-94
References_xml	– ident: ref13 – volume: 117 start-page: e680 year: 2006 ident: ref5 article-title: Laparotomy versus peritoneal drainage for necrotizing enterocolitis or isolated intestinal perforation in extremely low birth weight infants: outcomes through 18 months adjusted age publication-title: Pediatrics doi: 10.1542/peds.2005-1273 – volume: 107 start-page: e1 year: 2001 ident: ref3 article-title: Very low birth weight outcomes of the National Institute of Child Health and Human Development neonatal research network, January 1995 through December 1996 publication-title: Pediatrics doi: 10.1542/peds.107.1.e1 – volume: 28 start-page: 665 year: 2008 ident: ref16 article-title: Clinical parameters do not adequately predict outcome in necrotizing enterocolitis: a multi-institutional study publication-title: J Perinatol doi: 10.1038/jp.2008.119 – volume: 23 start-page: 278 year: 2003 ident: ref1 article-title: Necrotizing enterocolitis among neonates in the United States publication-title: Journal of perinatology doi: 10.1038/sj.jp.7210892 – ident: ref21 doi: 10.1016/j.jpeds.2013.10.091 – volume: 2 start-page: 2.2 issue: 13 year: 2011 ident: ref8 article-title: epicalc: Epidemiological calculator publication-title: R package version – ident: ref12 doi: 10.18637/jss.v011.i10 – ident: ref22 – volume: 187 start-page: 1 year: 1978 ident: ref7 article-title: Neonatal necrotizing enterocolitis. Therapeutic decisions based upon clinical staging publication-title: Annals of surgery doi: 10.1097/00000658-197801000-00001 – volume: 187 start-page: 1 year: 1978 ident: ref18 article-title: Neonatal necrotizing enterocolitis. Therapeutic decisions based upon clinical staging publication-title: Ann Surg doi: 10.1097/00000658-197801000-00001 – ident: ref20 doi: 10.1136/gutjnl-2013-305130 – ident: ref9 – volume: 8 start-page: 1661 year: 2013 ident: ref24 article-title: AKI in hospitalized children: epidemiology and clinical associations in a national cohort publication-title: Clin J Am Soc Nephrol doi: 10.2215/CJN.00270113 – volume: 22 start-page: 488 year: 1987 ident: ref17 article-title: Evaluation of factors in high risk neonatal necrotizing enterocolitis publication-title: J Pediatr Surg doi: 10.1016/S0022-3468(87)80202-6 – volume: 117 start-page: S6 year: 1990 ident: ref19 article-title: Pathology of neonatal necrotizing enterocolitis: a ten-year experience publication-title: J Pediatr doi: 10.1016/S0022-3476(05)81124-2 – volume: 39 start-page: 561 year: 1993 ident: ref15 article-title: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine publication-title: Clinical chemistry doi: 10.1093/clinchem/39.4.561 – volume: 241 start-page: 984 year: 2005 ident: ref4 article-title: Postoperative outcomes of extremely low birth-weight infants with necrotizing enterocolitis or isolated intestinal perforation: a prospective cohort study by the NICHD Neonatal Research Network publication-title: Annals of surgery doi: 10.1097/01.sla.0000164181.67862.7f – volume: 25 start-page: 1461 ident: ref23 article-title: Ling XB, Cohen H, Jin J, Lau I, Schilling J (2009) FDR made easy in differential feature discovery and correlation analyses publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp176 – volume: 105 start-page: 379 year: 2000 ident: ref2 article-title: The incidence of necrotizing enterocolitis after introducing standardized feeding schedules for infants between 1250 and 2500 grams and less than 35 weeks of gestation publication-title: Pediatrics doi: 10.1542/peds.105.2.379 – volume: 115 start-page: 696 year: 2005 ident: ref6 article-title: Neurodevelopmental and growth outcomes of extremely low birth weight infants after necrotizing enterocolitis publication-title: Pediatrics doi: 10.1542/peds.2004-0569 – ident: ref10 – volume: 2 start-page: 7 year: 2002 ident: ref11 article-title: Diagnostic checking in regression relationships publication-title: R news – ident: ref14 – reference: 15912048 - Ann Surg. 2005 Jun;241(6):984-9; discussion 989-94 – reference: 23833312 - Clin J Am Soc Nephrol. 2013 Oct;8(10):1661-9 – reference: 19376824 - Bioinformatics. 2009 Jun 1;25(11):1461-2 – reference: 24433829 - J Pediatr. 2014 Mar;164(3):607-12.e1-7 – reference: 24048736 - Gut. 2014 Aug;63(8):1284-92 – reference: 8472349 - Clin Chem. 1993 Apr;39(4):561-77 – reference: 11134465 - Pediatrics. 2001 Jan;107(1):E1 – reference: 3112356 - J Pediatr Surg. 1987 Jun;22(6):488-91 – reference: 16549503 - Pediatrics. 2006 Apr;117(4):e680-7 – reference: 413500 - Ann Surg. 1978 Jan;187(1):1-7 – reference: 18784730 - J Perinatol. 2008 Oct;28(10):665-74 – reference: 12774133 - J Perinatol. 2003 Jun;23(4):278-85 – reference: 10654959 - Pediatrics. 2000 Feb;105(2):379-84 – reference: 2362230 - J Pediatr. 1990 Jul;117(1 Pt 2):S6-13 – reference: 15741374 - Pediatrics. 2005 Mar;115(3):696-703
SSID	ssj0053866
Score	2.3429964
Snippet	Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model... Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of... BackgroundNecrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of... Background Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of...
SourceID	plos doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	e89860
SubjectTerms	Algorithms Biology Birth weight Colleges & universities Computer science Consortia Data processing Development and progression Diagnosis Diagnostic systems Discriminant analysis Disease Enterocolitis Enterocolitis, Necrotizing - diagnosis Enterocolitis, Necrotizing - pathology Epidemiology Female Gangrene Gastrointestinal diseases Health aspects Health risks Hospitals Humans Infant, Newborn Infants Laboratories Learning algorithms Machine learning Male Medical diagnosis Medical prognosis Medicine Morbidity Mortality Necrosis Necrotizing enterocolitis Neonates Newborn babies Pediatrics Process controls Prognosis Risk Smartphones Surgery Training Urine
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELbQigMXRHk1UMAgJOAQmsRJHB8XRAWXgnipt8jxo11pSVbJLhLc-OfM2E5oUKVyQDltPI525_l5M_5MyFPZpKVklYlzlrEY3-zF0lZFbAHuyjwxnDeJO2yCHx9XJyfiw7mjvrAnzNMDe8UdllJkCqqirASgD8ZkIVWT6kaKQjXcWMy-gHrGxZTPwRDFZRk2yjGeHga7vNx0rUFCU1E5Sso_hcjx9U9ZebFZd8NFkPPvzslzpejoBrkeMCRd-u--R66Y9ibZC1E60OeBSvrFLfJrSbEFNNY9JjUq16ddv9qefaMjSwQULjpujqSy1TQ4Rdf_oNY40s-BAq6lgBOp9m15q8FJYmeX_9RZ2gL67Larn_g8ZPmEsuga64bb5MvRm8-v38bh0IVYlSLbxjLRDU-04NpIWSWyzEXSZMqmuYW1CKuKwjCDpG_C5MYCmoEVWmkZwJBUsQbC-Q5ZtKDmfUIbnpsiYVbDrDw1TKjUprbShSmyRKdZRNhogVoFRnI8GGNdu9dsHFYmXqE12q0OdotIPM3aeEaOS-RfoXEnWeTTdjfAy-rgZfVlXhaRR-gatd-cOmWFeoncRnCJNCJPnARyarTYtHMqd8NQv3v_9R-EPn2cCT0LQrYDdSgZNkrAb0KurpnkwUwSMoOaDe-jI49aGWoA_yzjgOkFzByd--Lhx9MwPhQb8VrT7ZwMQD78VyAid30sTJrN8gKyfwUa57Momal-PtKuzhylORPgI4m49z9sdZ9cA1Sbe96BA7LY9jvzgFxV38Hp-4cuT_wGFRVw7w priority: 102 providerName: Directory of Open Access Journals – databaseName: Public Library of Science (PLoS) Journals Open Access dbid: FPL link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELdQQYgXxsrHOgYYhAQ8BJI4iePHQqmYNErFl_ZmOY69VeqSKkmR4I3_nLPjBDJtApSnxOcoPd-df1eff0boqciCRJBUeREJiWdW9jyh09jTAHdF5CtKM98eNkEXi_T4mC1_J4rnVvAJDV45nb7clIUyZKQsTSBFvxqSJDElXPPlURd5wXeTxG2Pu6znYPqxLP19LB5t1mV9EdA8Xy_5xwQ03_nfT7-FbjqoiaetbeyiK6oYo53uGAfsvHqMrr936-tjtOse1vi546N-cRv9nOKZaIQ3q0xkxNP1SVmtmtMzfOioJmD2w45fdI1FkeOj1rLK6js2EHMLKT0GcIwBbOJZW9u3qq3ksirdXanxAiBs2ax-mPfZYoVS2uq8-g76Mn_7-c07z53c4MmEhY0n_Dyjfs5oroRIfZFEzM9CqYNIQ0JD0jhWRBnmOKYipQESQZqXaAJYJpAkg5hwF40KUNoewhmNVOwTnUOvKFCEyUAHOs1jFYd-HoQTRLoB5dLRmpvTNdbcrtVRSG9adXMzCtyNwgR5fa9NS-vxF_nXxlZ6WUPKbR_AcHPn4zwRLJQA4ETKACgTImIhsyDPBItlRpWeoEfG0ni7w7UPLXxqCJLgYsEEPbEShpijMJU_J2Jb1_zww9d_EPr0cSD0zAnpEtQhhdttAb_JEH4NJA8GkhBe5KB5z_hFp5WaQwZBQgqJAYOena9c3Py4bzYvNdV8hSq3VgZwo_lrYYLuta7VazaMYphCUtA4HTjdQPXDlmJ1annRCQMb8dn-5V98H90AwBu1lAQHaNRUW_UAXZPfwJSrhzaY_ALJyHS6 priority: 102 providerName: Public Library of Science
Title	A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis
URI	https://www.ncbi.nlm.nih.gov/pubmed/24587080 https://www.proquest.com/docview/1503271929 https://www.proquest.com/docview/1504158409 https://pubmed.ncbi.nlm.nih.gov/PMC3938509 https://doaj.org/article/6a92c635a8904533a5acb1dba95cb7ef http://dx.doi.org/10.1371/journal.pone.0089860
Volume	9
WOSCitedRecordID	wos000332396200089&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: P5Z dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M0K dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7P dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M7S dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Environmental Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PATMY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/environmentalscience providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Materials Science Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KB. dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/materialsscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7RV dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: PIMPY dateStart: 20061201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVATS databaseName: Public Library of Science (PLoS) Journals Open Access customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: FPL dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.plos.org/publications/ providerName: Public Library of Science
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwELZYhxAvjJUfK4xiEBLwkC2Jkzh-Qu26imlriTqYCi-R4zhdpZKUpkWCN_5zzo7bETQBEqp0UuNz2lzO58_2-TNCL3jiBJyE0vKISyy1smfxLPStDOAu92xJaWLrwybocBiOxywyE26lSatcx0QdqNNCqDnyQwAuxKWAR9ib-RdLnRqlVlfNERpbaFuxJBCduhetIzG05SAw2-UIdQ7N2zmYF7lUtKYs1MSUV92RZu3fxObGfFaU1wHP3_Mnf-mQ-jv_-yh30R0DRXGn8p1ddEPmTbSzPuYBm1bfRLcGZv29iXbNxRK_MnzVr--hHx3c40tu9RYqcuLObAI_trz8jE8MFQX0jtjwj84wz1N8VnlesfiGFQRdwZAfA3jGAEZxr8r9m5ZaM1oU5luR4SFA3GI5_a7up5MZCqGz98r76EP_-P3RW8uc7GCJgLlLi9tpQu2U0VRyHto88JiduCJzvAwGPCT0fUmkYpZj0pMZQCYYBgYZAazjCJJAzHiAGjm8xT2EE-pJsGeWQi3PkYQJJ3OyMPWl79qp47YQWb_gWBjac3X6xizWa3kUhj-VuWPlFrFxixayNrXmFe3HX_S7ync2uoq0W18oFpPYxIA44MwVAPB4yABIE8J9LhInTTjzRUJl1kJPlefF1Q7YTeiJO4pACT7MaaHnWkMRd-QqM2jCV2UZn7y7-Ael81FN6aVRygowh-BmNwY8kyIEq2nu1zQh_Iha8Z5qJ2urlPGVd0PNtf9fX_xsU6xuqrL9clmstA7gSjX10EIPq6a2sazr-dDFhGBxWmuENdPXS_LppeZNJwx8xGaP_vy3HqPbAIq9irZgHzWWi5V8gm6Kr-DOizbaoqMLJcdUyxBkeOS00Xb3eBiN2npOB2Q_OgN52j0AObBP2zowaXkOMvI_QY3oZBB9_AnoGpLz
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFLdGQcCFsfKxwmAGgYBDWBIncXxAqFCmVdvKtI1pt-A4TlepJCVpQePGP8TfyLPjZARNwGUH1FPjZyt5eZ_x8-8h9ITHTsBJKC2PuMRSO3sWT0PfSiHc5Z4tKY1t3WyCjkbh8THbW0I_6rMwqqyytonaUCe5UN_INyBwIS6FeIS9nn22VNcotbtat9CoxGJbnn6FlK18NRzA-33qupvvDt9uWaargCUC5s4tbicxtRNGE8l5aPPAY3bsitTxUgi2Sej7kkiFasakJ1Nw15CCBCkBP-sIEoO8wrqX0GWw444qIaP7R7XlB9sRBOZ4HqHOhpGGl7M8kwpGlYUaCPPM_ekuAY0v6MymeXleoPt7veYvDnBz-X9j3U10w4TauF_pxgpaklkXLddtLLCxal10ddfUF3TRirlY4ucGj_vFLfS9jwd8zq1BoTwD7k_H8HDzk094aKA2wPtjg686xTxL8E6lWXlxilWIvShgQUgOMATbeFDVNk5KTblX5OZfnuIRhPD5fPJNraeLNXKhqxPL2-jDhTDqDupkIDWrCMfUk_D-0gRmeY4kTDipk4aJL33XThy3h0gtUJEwsO6qu8g00nuVFNK7it2REsPIiGEPWc2sWQVr8hf6N0pWG1oFSq4v5MU4MjYuCjhzBQSwPGSQKBDCfS5iJ4k580VMZdpD60rSo-qEb2Nao74CiIIfc3rosaZQwCSZqnwa80VZRsP3R_9AdLDfInpmiNIc2CG4OW0Cz6QAz1qUay1KMK-iNbyq9LLmShmdaRPMrPXt_OFHzbBaVFUzZjJfaBqIm9WnlR66W6l2w1nX88GFhsBx2lL6FuvbI9nkROPCEwYyYrN7f76tdXRt63B3J9oZjrbvo-uQAHgVRMMa6syLhXyArogvINrFQ23cMPp40SbhJwIK4X8
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFLdGQRMXxsrHCoMZBAIOoUmcLx8QKisV1UaZGKDdguPYXaWSlKQFjRv_Fn8dz46TETQBlx1QT42freTl5_ee4-ffQ-gBS5yAkUhYHnGJpXb2LCYj35IQ7jLPFmGY2LrYRDiZREdH9GAN_ajPwqi0ytomakOd5lx9I-9D4ELcEOIR2pcmLeJgOHq--GypClJqp7Uup1FBZE-cfIXlW_lsPIR3_dB1Ry_f7b6yTIUBiwfUXVrMTpPQTmmYCsYimwUetROXS8eTEHiTyPcFEYrhjApPSHDdsBwJJAGf63CSAHZh3AvoYkgAxeqU-m6TXgJ2JAjMUT0SOn2DjKeLPBOKUpVGmhTz1BXqigGNX-gs5nl5VtD7e-7mL85wtPE_q_EqumJCcDyo5swmWhNZF23U5S2wsXZdtP7a5B100aa5WOLHhqf7yTX0fYCHbMmsYaE8Bh7Mp_Bwy-NPeGwoOCAqwIZ3dY5ZluL9asblxQlWofeqgAFh0YAhCMfDKudxVmrJgyI3_3KJJxDa58vZNzWeTuLIuc5aLK-j9-eiqBuokwGCthBOQk_Au5Qp9PIcQSh3pCOj1Be-a6eO20OkBlfMDd27qjoyj_UeZgjLvkrdsYJkbCDZQ1bTa1HRnfxF_oXCbSOryMr1hbyYxsb2xQGjLofAlkUUFhCEMJ_xxEkTRn2ehEL20I5CfVyd_G1MbjxQxFHwo04P3dcSirAkU5idslVZxuM3H_5B6PBtS-iREZI5qIMzcwoFnkkRobUkt1uSYHZ5q3lLzdFaK2V8OrOgZz33zm6-1zSrQVWWYybylZaBeFp9cumhm9U0bzTrej641gg0HrYMQEv17ZZsdqz54gkFjNj01p9vawetgyWI98eTvdvoMqwLvIq5YRt1lsVK3EGX-BdAdnFX2zmMPp63RfgJeEbp2g
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Data-Driven+Algorithm+Integrating+Clinical+and+Laboratory+Features+for+the+Diagnosis+and+Prognosis+of+Necrotizing+Enterocolitis&rft.jtitle=PloS+one&rft.au=Ji%2C+Jun&rft.au=Ling%2C+Xuefeng+B.&rft.au=Zhao%2C+Yingzhen&rft.au=Hu%2C+Zhongkai&rft.date=2014-02-28&rft.issn=1932-6203&rft.eissn=1932-6203&rft.volume=9&rft.issue=2&rft.spage=e89860&rft_id=info:doi/10.1371%2Fjournal.pone.0089860&rft.externalDBID=n%2Fa&rft.externalDocID=10_1371_journal_pone_0089860
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon