Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm
Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conduc...
Uložené v:
| Vydané v: | BMC medical informatics and decision making Ročník 22; číslo 1; s. 137 - 10 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
18.05.2022
BioMed Central Ltd Springer Nature B.V BMC |
| Predmet: | |
| ISSN: | 1472-6947, 1472-6947 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Background
Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning
preoperative
model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models.
Methods
We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer–Lemeshow goodness-of-fit test) in the validation datasets.
Results
Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer–Lemeshow
p
= 0.804, and AUC = 0.74, Hosmer–Lemeshow
p
= 0.347, respectively).
Conclusions
We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. |
|---|---|
| AbstractList | Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models.BACKGROUNDAcute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models.We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets.METHODSWe conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets.Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively).RESULTSOf 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively).We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies.CONCLUSIONSWe demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer–Lemeshow goodness-of-fit test) in the validation datasets. Results Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer–Lemeshow p = 0.804, and AUC = 0.74, Hosmer–Lemeshow p = 0.347, respectively). Conclusions We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Abstract Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer–Lemeshow goodness-of-fit test) in the validation datasets. Results Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer–Lemeshow p = 0.804, and AUC = 0.74, Hosmer–Lemeshow p = 0.347, respectively). Conclusions We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets. Results Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively). Conclusions We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Keywords: Cardiac surgery-associated acute kidney injury, Machine Learning, Random Forests, Data mining, Predictive modeling Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets. Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively). We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer-Lemeshow goodness-of-fit test) in the validation datasets. Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer-Lemeshow p = 0.804, and AUC = 0.74, Hosmer-Lemeshow p = 0.347, respectively). We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conducted a retrospective study of adult patients who underwent major cardiac surgery requiring cardiopulmonary bypass between November 1st, 2009 and March 31st, 2015. AKI was defined according to the KDIGO criteria as stage 1 or greater, within 7 days of surgery. We randomly split the cohort into derivation and validation datasets. We developed three AKI risk models: (1) a hybrid machine learning (ML) algorithm, using Random Forests for variable selection, followed by high performance logistic regression; (2) a traditional logistic regression model and (3) an enhanced logistic regression model with 500 bootstraps, with backward variable selection. For each model, we assigned risk scores to each of the retained covariate and assessed model discrimination (C statistic) and calibration (Hosmer–Lemeshow goodness-of-fit test) in the validation datasets. Results Of 6522 included patients, 1760 (27.0%) developed AKI. The best performance was achieved by the hybrid ML algorithm to predict AKI of any severity. The ML and enhanced statistical models remained robust after internal validation (C statistic = 0.75; Hosmer–Lemeshow p = 0.804, and AUC = 0.74, Hosmer–Lemeshow p = 0.347, respectively). Conclusions We demonstrated that a hybrid ML model provides higher accuracy without sacrificing parsimony, computational efficiency, or interpretability, when compared with parametric statistical models. This score-based model can easily be used at the bedside to identify high-risk patients who may benefit from intensive perioperative monitoring and personalized management strategies. |
| ArticleNumber | 137 |
| Audience | Academic |
| Author | Petrosyan, Yelena Sun, Louise Y. Mesana, Thierry G. |
| Author_xml | – sequence: 1 givenname: Yelena surname: Petrosyan fullname: Petrosyan, Yelena organization: Cardiocore Big Data Research Unit, University of Ottawa Heart Institute – sequence: 2 givenname: Thierry G. surname: Mesana fullname: Mesana, Thierry G. organization: Cardiocore Big Data Research Unit, University of Ottawa Heart Institute – sequence: 3 givenname: Louise Y. surname: Sun fullname: Sun, Louise Y. email: lsun@ottawaheart.ca organization: Cardiocore Big Data Research Unit, University of Ottawa Heart Institute, Division of Cardiac Anesthesiology, University of Ottawa Heart Institute, School of Epidemiology and Public Health, University of Ottawa |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35585624$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9ks1vFCEYxiemxn7oP-DBkHjxMi0wAwMeTJpGbZMmetAzecvHLNsZqDDTZv972d3adhvTcIDA8_xeXngOq70Qg62q9wQfEyL4SSZUElJjSmtMBJP13avqgLQdrblsu70n6_3qMOclxqQTDXtT7TeMCcZpe1D1P5M1Xk8-BhQdAj1PFl17E-wK-bCc0woln68RuMkmpCEZDxrlOfU2rT6jOfvQI0CL1VXyBo2gFz5YNFhIYXMy9DH5aTG-rV47GLJ9dz8fVb-_ff11dl5f_vh-cXZ6WWuOu6kmGhuuWwKcATENaU3XOWZl0xEJxjYYc9a6hnEinGBUs1ZYKQyV1BloqGmOqost10RYqpvkR0grFcGrzUZMvYI0eT1YZQxoyTomhYRWcyccxq0k2BntMIi2sL5sWTfz1WiNtmFKMOxAd0-CX6g-3qryK6JjogA-3QNS_DPbPKnRZ22HAYKNc1aUcy4xb6ks0o_PpMs4p1CeaqNqOGOEPap6KA344GKpq9dQddrhwqElDEV1_B9VGcaOXpcMOV_2dwwfnjb60OG_mBQB3Qp0ijkn6x4kBKt1FtU2i6rQ1CaL6q6YxDOT9hOsk1au44eXrc3WmkudUKL2-BovuP4CBjDy5g |
| CitedBy_id | crossref_primary_10_2147_CLEP_S404580 crossref_primary_10_3389_fmed_2023_1050255 crossref_primary_10_1016_j_jclinane_2025_111782 crossref_primary_10_1186_s12911_024_02758_y crossref_primary_10_1053_j_jvca_2023_06_045 crossref_primary_10_1097_ALN_0000000000004764 |
| Cites_doi | 10.7326/0003-4819-128-3-199802010-00005 10.4103/0971-9784.191578 10.1093/bib/bbs034 10.1023/A:1010933404324 10.1161/CIRCULATIONAHA.108.786913 10.1056/NEJMicm064659 10.1681/ASN.2003100875 10.1097/00000542-200102000-00006 10.1017/ice.2015.327 10.1016/j.athoracsur.2011.09.010 10.1161/circ.54.3.947585 10.1001/jama.297.16.1801 10.1002/art.21695 10.1016/j.amjsurg.2013.04.006 10.1046/j.1492-7535.2003.00029.x 10.1371/journal.pone.0217057 10.1097/ALN.0000000000002298 10.1191/0267659105pf829oa 10.1038/kisup.2012.1 10.1038/sj.ki.5002419 10.1186/s13054-014-0606-x 10.1186/1756-0500-4-299 10.1002/sim.7591 10.7717/peerj.6339 10.1016/j.athoracsur.2011.09.073 10.1002/sim.3104 10.1186/s12859-018-2264-5 10.1016/j.jtcvs.2013.06.049 10.1016/j.ejcts.2011.06.015 10.1002/sim.1742 10.1016/0003-4975(96)00055-0 10.1016/j.ahj.2003.12.042 10.1016/j.pmrj.2013.07.007 10.1681/ASN.2011090940 10.1016/j.jcrc.2015.11.004 10.1177/070674370705200210 10.1371/journal.pone.0098028 10.1053/j.ajkd.2009.01.267 10.1186/cc7894 10.1371/journal.pone.0096385 10.1007/s12630-014-0302-y 10.1016/j.athoracsur.2010.08.018 10.1161/CIRCULATIONAHA.106.635573 10.1007/s10916-011-9730-1 10.1186/cc13041 10.1371/journal.pone.0149089 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2022 2022. The Author(s). COPYRIGHT 2022 BioMed Central Ltd. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2022 – notice: 2022. The Author(s). – notice: COPYRIGHT 2022 BioMed Central Ltd. – notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7X7 7XB 88C 88E 8AL 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M0T M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/s12911-022-01859-w |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Database ProQuest Central Technology collection Natural Science Collection ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni Edition) Healthcare Administration Database PML(ProQuest Medical Library) Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest - 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 ProQuest Central Basic 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) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest Health Management ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals (DOAJ) 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 | Medicine |
| EISSN | 1472-6947 |
| EndPage | 10 |
| ExternalDocumentID | oai_doaj_org_article_ddac9575989a4c6f8f004910fdcf0a84 PMC9118758 A704292022 35585624 10_1186_s12911_022_01859_w |
| Genre | Journal Article |
| GeographicLocations | Canada United States--US |
| GeographicLocations_xml | – name: Canada – name: United States--US |
| GrantInformation_xml | – fundername: University of Ottawa funderid: http://dx.doi.org/10.13039/100008572 – fundername: ; |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIWK ACPRK ACUHS ADBBV ADUKV AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS AQUVI ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO IHR INH INR ITC K6V K7- KQ8 LK8 M0T M1P M48 M7P M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK FR3 JQ2 K9. L7M L~C L~D M0N P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c607t-1c0d6c41a65a1d314d77f5e93719ade300654f35618f852c548e98d292fda32d3 |
| IEDL.DBID | M0T |
| ISICitedReferencesCount | 11 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000797513300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1472-6947 |
| IngestDate | Fri Oct 03 12:43:44 EDT 2025 Tue Nov 04 01:59:10 EST 2025 Fri Sep 05 11:31:33 EDT 2025 Mon Nov 24 18:15:13 EST 2025 Tue Nov 11 10:22:58 EST 2025 Tue Nov 04 17:13:50 EST 2025 Mon Jul 21 06:00:20 EDT 2025 Sat Nov 29 06:13:56 EST 2025 Tue Nov 18 21:59:30 EST 2025 Sat Sep 06 07:31:42 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Cardiac surgery-associated acute kidney injury Random Forests Predictive modeling Data mining Machine Learning |
| Language | English |
| License | 2022. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c607t-1c0d6c41a65a1d314d77f5e93719ade300654f35618f852c548e98d292fda32d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2666365515?pq-origsite=%requestingapplication% |
| PMID | 35585624 |
| PQID | 2666365515 |
| PQPubID | 42572 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_ddac9575989a4c6f8f004910fdcf0a84 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9118758 proquest_miscellaneous_2666906429 proquest_journals_2666365515 gale_infotracmisc_A704292022 gale_infotracacademiconefile_A704292022 pubmed_primary_35585624 crossref_primary_10_1186_s12911_022_01859_w crossref_citationtrail_10_1186_s12911_022_01859_w springer_journals_10_1186_s12911_022_01859_w |
| PublicationCentury | 2000 |
| PublicationDate | 2022-05-18 |
| PublicationDateYYYYMMDD | 2022-05-18 |
| PublicationDate_xml | – month: 05 year: 2022 text: 2022-05-18 day: 18 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC medical informatics and decision making |
| PublicationTitleAbbrev | BMC Med Inform Decis Mak |
| PublicationTitleAlternate | BMC Med Inform Decis Mak |
| PublicationYear | 2022 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | J Cremer (1859_CR43) 1996; 61 JR Brown (1859_CR4) 2007; 116 J Li (1859_CR17) 2016; 11 A Liam (1859_CR32) 2002; 2 SU Nigwekar (1859_CR50) 2009; 54 CV Thakar (1859_CR11) 2003; 7 DT Tran (1859_CR25) 2012; 41 MN Wright (1859_CR34) 2019; 7 A Parolari (1859_CR5) 2012; 93 Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group (1859_CR19) 2012; 2 MM Ward (1859_CR16) 2006; 55 DN Wijeysundera (1859_CR10) 2007; 297 CM Mangano (1859_CR2) 1998; 128 P Jorge-Monjas (1859_CR47) 2016; 31 AM Robert (1859_CR3) 2010; 90 C van Walraven (1859_CR37) 2016; 37 JY Dupuis (1859_CR24) 2001; 94 H Palomba (1859_CR13) 2007; 72 K Karkouti (1859_CR28) 2009; 119 LM Sullivan (1859_CR38) 2004; 23 MN Machado (1859_CR41) 2014; 9 PC Austin (1859_CR30) 2008; 27 L Campeau (1859_CR26) 1976; 54 J Maroco (1859_CR45) 2011; 4 L Breiman (1859_CR31) 2001; 45 S Doerken (1859_CR35) 2019; 14 K Birnie (1859_CR20) 2014; 18 LY Sun (1859_CR18) 2018; 129 CE Lok (1859_CR14) 2004; 148 SY Ng (1859_CR22) 2014; 147 FT Billings (1859_CR21) 2012; 23 DL Streiner (1859_CR39) 2007; 52 A Ozcift (1859_CR46) 2012; 36 C Ortega-Loubon (1859_CR8) 2016; 19 I Bahar (1859_CR29) 2005; 20 K Karkouti (1859_CR23) 2015; 62 FJ Abelha (1859_CR7) 2009; 13 M Legrand (1859_CR27) 2013; 17 R Couronne (1859_CR49) 2018; 19 HC Kang (1859_CR42) 2007; 357 1859_CR36 KL Sainani (1859_CR40) 2013; 5 HS Gurm (1859_CR15) 2014; 9 WG Touw (1859_CR33) 2013; 14 RH Mehta (1859_CR9) 2006; 114 M Biteker (1859_CR6) 2014; 207 I Sgouralis (1859_CR44) 2017; 34 SC Huen (1859_CR12) 2012; 93 BG Loef (1859_CR1) 2005; 16 PC Austin (1859_CR48) 2018; 37 |
| References_xml | – volume: 128 start-page: 194 issue: 3 year: 1998 ident: 1859_CR2 publication-title: Ann Intern Med doi: 10.7326/0003-4819-128-3-199802010-00005 – volume: 19 start-page: 687 issue: 4 year: 2016 ident: 1859_CR8 publication-title: Ann Card Anaesth doi: 10.4103/0971-9784.191578 – volume: 14 start-page: 315 issue: 3 year: 2013 ident: 1859_CR33 publication-title: Brief Bioinform doi: 10.1093/bib/bbs034 – volume: 45 start-page: 5 year: 2001 ident: 1859_CR31 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 119 start-page: 495 issue: 4 year: 2009 ident: 1859_CR28 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.108.786913 – volume: 357 issue: 18 year: 2007 ident: 1859_CR42 publication-title: N Engl J Med doi: 10.1056/NEJMicm064659 – volume: 116 start-page: I139 issue: 11 Suppl year: 2007 ident: 1859_CR4 publication-title: Circulation – volume: 16 start-page: 195 issue: 1 year: 2005 ident: 1859_CR1 publication-title: J Am Soc Nephrol doi: 10.1681/ASN.2003100875 – volume: 94 start-page: 194 issue: 2 year: 2001 ident: 1859_CR24 publication-title: Anesthesiology doi: 10.1097/00000542-200102000-00006 – volume: 37 start-page: 455 issue: 4 year: 2016 ident: 1859_CR37 publication-title: Infect Control Hosp Epidemiol doi: 10.1017/ice.2015.327 – volume: 93 start-page: 337 issue: 1 year: 2012 ident: 1859_CR12 publication-title: Ann Thorac Surg doi: 10.1016/j.athoracsur.2011.09.010 – volume: 54 start-page: 522 issue: 3 year: 1976 ident: 1859_CR26 publication-title: Circulation doi: 10.1161/circ.54.3.947585 – volume: 297 start-page: 1801 issue: 16 year: 2007 ident: 1859_CR10 publication-title: JAMA doi: 10.1001/jama.297.16.1801 – volume: 55 start-page: 74 issue: 1 year: 2006 ident: 1859_CR16 publication-title: Arthritis Rheum doi: 10.1002/art.21695 – volume: 207 start-page: 53 issue: 1 year: 2014 ident: 1859_CR6 publication-title: Am J Surg doi: 10.1016/j.amjsurg.2013.04.006 – volume: 7 start-page: 143 issue: 2 year: 2003 ident: 1859_CR11 publication-title: Hemodial Int doi: 10.1046/j.1492-7535.2003.00029.x – volume: 14 issue: 5 year: 2019 ident: 1859_CR35 publication-title: PLoS ONE doi: 10.1371/journal.pone.0217057 – volume: 129 start-page: 440 issue: 3 year: 2018 ident: 1859_CR18 publication-title: Anesthesiology doi: 10.1097/ALN.0000000000002298 – volume: 20 start-page: 317 issue: 6 year: 2005 ident: 1859_CR29 publication-title: Perfusion doi: 10.1191/0267659105pf829oa – volume: 2 start-page: 1 year: 2012 ident: 1859_CR19 publication-title: Kidney Int Suppl doi: 10.1038/kisup.2012.1 – volume: 72 start-page: 624 issue: 5 year: 2007 ident: 1859_CR13 publication-title: Kidney Int doi: 10.1038/sj.ki.5002419 – volume: 34 start-page: 313 issue: 3 year: 2017 ident: 1859_CR44 publication-title: Math Med Biol – volume: 18 start-page: 606 issue: 6 year: 2014 ident: 1859_CR20 publication-title: Crit Care doi: 10.1186/s13054-014-0606-x – volume: 4 start-page: 299 year: 2011 ident: 1859_CR45 publication-title: BMC Res Notes doi: 10.1186/1756-0500-4-299 – volume: 37 start-page: 1405 issue: 8 year: 2018 ident: 1859_CR48 publication-title: Stat Med doi: 10.1002/sim.7591 – ident: 1859_CR36 – volume: 7 year: 2019 ident: 1859_CR34 publication-title: PeerJ doi: 10.7717/peerj.6339 – volume: 93 start-page: 584 issue: 2 year: 2012 ident: 1859_CR5 publication-title: Ann Thorac Surg doi: 10.1016/j.athoracsur.2011.09.073 – volume: 27 start-page: 3286 issue: 17 year: 2008 ident: 1859_CR30 publication-title: Stat Med doi: 10.1002/sim.3104 – volume: 19 start-page: 270 issue: 1 year: 2018 ident: 1859_CR49 publication-title: BMC Bioinformatics doi: 10.1186/s12859-018-2264-5 – volume: 147 start-page: 1875 issue: 6 year: 2014 ident: 1859_CR22 publication-title: J Thorac Cardiovasc Surg doi: 10.1016/j.jtcvs.2013.06.049 – volume: 2 start-page: 315 issue: 3 year: 2002 ident: 1859_CR32 publication-title: R News – volume: 41 start-page: 307 issue: 2 year: 2012 ident: 1859_CR25 publication-title: Eur J Cardiothorac Surg doi: 10.1016/j.ejcts.2011.06.015 – volume: 23 start-page: 1631 issue: 10 year: 2004 ident: 1859_CR38 publication-title: Stat Med doi: 10.1002/sim.1742 – volume: 61 start-page: 1714 issue: 6 year: 1996 ident: 1859_CR43 publication-title: Ann Thorac Surg doi: 10.1016/0003-4975(96)00055-0 – volume: 148 start-page: 430 issue: 3 year: 2004 ident: 1859_CR14 publication-title: Am Heart J doi: 10.1016/j.ahj.2003.12.042 – volume: 5 start-page: 791 issue: 9 year: 2013 ident: 1859_CR40 publication-title: PM&R doi: 10.1016/j.pmrj.2013.07.007 – volume: 23 start-page: 1221 issue: 7 year: 2012 ident: 1859_CR21 publication-title: J Am Soc Nephrol doi: 10.1681/ASN.2011090940 – volume: 31 start-page: 130 issue: 1 year: 2016 ident: 1859_CR47 publication-title: J Crit Care doi: 10.1016/j.jcrc.2015.11.004 – volume: 52 start-page: 121 issue: 2 year: 2007 ident: 1859_CR39 publication-title: Can J Psychiatry doi: 10.1177/070674370705200210 – volume: 9 issue: 5 year: 2014 ident: 1859_CR41 publication-title: PLoS ONE doi: 10.1371/journal.pone.0098028 – volume: 54 start-page: 413 issue: 3 year: 2009 ident: 1859_CR50 publication-title: Am J Kidney Dis doi: 10.1053/j.ajkd.2009.01.267 – volume: 13 start-page: R79 issue: 3 year: 2009 ident: 1859_CR7 publication-title: Crit Care doi: 10.1186/cc7894 – volume: 9 issue: 5 year: 2014 ident: 1859_CR15 publication-title: PLoS ONE doi: 10.1371/journal.pone.0096385 – volume: 62 start-page: 377 issue: 4 year: 2015 ident: 1859_CR23 publication-title: Can J Anaesth doi: 10.1007/s12630-014-0302-y – volume: 90 start-page: 1939 issue: 6 year: 2010 ident: 1859_CR3 publication-title: Ann Thorac Surg doi: 10.1016/j.athoracsur.2010.08.018 – volume: 114 start-page: 2208 issue: 21 year: 2006 ident: 1859_CR9 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.106.635573 – volume: 36 start-page: 2577 issue: 4 year: 2012 ident: 1859_CR46 publication-title: J Med Syst doi: 10.1007/s10916-011-9730-1 – volume: 17 start-page: R220 issue: 5 year: 2013 ident: 1859_CR27 publication-title: Crit Care doi: 10.1186/cc13041 – volume: 11 issue: 2 year: 2016 ident: 1859_CR17 publication-title: PLoS ONE doi: 10.1371/journal.pone.0149089 |
| SSID | ssj0017835 |
| Score | 2.3748796 |
| Snippet | Background
Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning
preoperative
model... Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict... Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model... Abstract Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 137 |
| SubjectTerms | Acute Kidney Injury - diagnosis Acute Kidney Injury - epidemiology Acute Kidney Injury - etiology Acute renal failure Adult Algorithms Artificial intelligence Calibration Cardiac surgery-associated acute kidney injury Cardiac Surgical Procedures - adverse effects Cardiovascular disease Care and treatment Classification Computer applications Coronary artery bypass Data mining Datasets Diagnosis Feature selection Generalized linear models Goodness of fit Health aspects Health Informatics Health risk assessment Health risks Heart Heart surgery Humans Information Systems and Communication Service Kidneys Learning algorithms Machine Learning Management of Computing and Information Systems Mathematical models Medicine Medicine & Public Health Methods Model accuracy Mortality Patient outcomes Patients Predictive modeling Random Forests Regression models Retrospective Studies Risk Risk Assessment Risk Factors Risk groups Statistical analysis Statistical methods Statistical models Statistical tests Surgery |
| SummonAdditionalLinks | – databaseName: DOAJ: Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQhRAXVN6BgoyExAGiJk784lYQFReqHkDqzXL82F3aZtFutlX_PTNOsjRFwIVbFDuSPY94bM98HyGva1YrL53Pay5jXoeG56rxLBc2Ki3LqArfJLIJeXSkTk708TWqL8wJ6-GBe8Hte2-dRhZJpW3tRFQRg9qyiN7FwqqEBFpIPW6mhvsDPM8YS2SU2F_DqoZHgQzTEBTX-eVkGUpo_b__k68tSjcTJm_cmqbF6HCX3BuiSHrQj_4-uRXaB-TOl-Ge_CGZHa_wGYVOl5Fat-kCPV34NlzRRfsd5Egxp5wmhnDqkpU4uu5LpN9TTIafUUvnV1jPRc9TwmWgA8MEtJzNlqtFNz9_RL4dfvr68XM-UCrkThSyy0tXeOHq0gpuS1-VtZcy8oCgeNr6UKVa01hBUKWi4szBfiZo5Zlm0duK-eox2WmXbXhKaGNr8HfNQ9OA4zdCqwpCCYiOuCtE4VlGylHCxg1440h7cWbSvkMJ02vFgFZM0oq5zMjb7Tc_erSNv_b-gIrb9kSk7PQC7McM9mP-ZT8ZeYNqN-jPMDxnh7IEmCQiY5kDWSRGLwYT2pv0BD900-bRcMzwH1gbCH9EJSAq5Rl5tW3GLzG3rQ3LTd9H4z5QZ-RJb2fbKSH4PUSoMEo5scDJnKct7WKeUMI1EslzlZF3o63-GtafZfrsf8j0ObnLkq_xvFR7ZKdbbcILcttddIv16mXy1J_Ya0Bz priority: 102 providerName: Directory of Open Access Journals – databaseName: Springer Nature - Connect here FIRST to enable access dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3JbtUw0IKCUC_sS6AgIyFxgKiJEzs2t4KouFBVYlFvluPlvUCbVEkeVf8ej7NAyiLBLYrHkmc8M56xZ0HoWU5ybgpt4pwWLs5tSWNeGhIz5bgoUscTU4ZmE8XBAT86EodjUlg3RbtPT5JBUwex5my38ycTXOcRCCXgVMRnl9EVCtVmwEf_8Hl-O4C7jCk95rfzFkdQqNT_qz7-6UC6GCx54cU0HET7N_4PhZvo-mh44r2BU26hS7a-ja69H5_W76DVYQvfsE-4cVjpTW_x18rU9hxX9RdPegxh6Dg0Fcc6MJbG3ZBV_QpD_PwKK7w-hxQwfBJiNC0em1L4keNV01b9-uQu-rT_9uObd_HYhSHWLCn6ONWJYTpPFaMqNVmam6Jw1EIdPaGMzUJ6qsu8HcYdp0R7F8gKboggzqiMmOwe2qqb2j5AuFS5VxGC2rL0uqJkgmfe-vAGFdUJSwyJUDptjNRjiXLolHEsg6vCmRwoKD0FZaCgPIvQi3nO6VCg46_Qr2G_Z0gorh1-NO1KjrIqjVFaQONSLlSumeMO_Kg0cUa7RPE8Qs-BWySoAL88rcZMBo8kFNOSe0USmoARj9DOAtKLrl4OT_wmR9XRSW8xsYx5Q5ZG6Ok8DDMhHK62zWaAEeA6igjdH9hzRgnq5Xuj1q-yWDDuAuflSF2tQ2FxAb3nKY_Qy4l9fyzrzzR9-G_gj9A2CRJA45TvoK2-3djH6Kr-1ldd-ySI8ncrRUTQ priority: 102 providerName: Springer Nature |
| Title | Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm |
| URI | https://link.springer.com/article/10.1186/s12911-022-01859-w https://www.ncbi.nlm.nih.gov/pubmed/35585624 https://www.proquest.com/docview/2666365515 https://www.proquest.com/docview/2666906429 https://pubmed.ncbi.nlm.nih.gov/PMC9118758 https://doaj.org/article/ddac9575989a4c6f8f004910fdcf0a84 |
| Volume | 22 |
| WOSCitedRecordID | wos000797513300002&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: PRVADU databaseName: BioMedCentral customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: Directory of Open Access Journals (DOAJ) customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: DOA dateStart: 20010101 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: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Healthcare Administration Database customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: M0T dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthmanagement providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1472-6947 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017835 issn: 1472-6947 databaseCode: RSV dateStart: 20011201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELfYhhAvfH8ERmUkJB4gWr4c27ygDW0CoVbRGKjwYjl23Ba2dPSDaf89d07akSH2wouV1I7k653vzufz_Qh5kSWZsNzYMGPchVlVslCUNglz7YTksRORLT3YBB8MxHAoizbgNm_TKlc60StqOzUYI98BQ5KnOdh39vb0Z4ioUXi62kJobJCtGFxrRDDoR0frUwSMaqwuyoh8Zw62DQOCCSYjCCbDs44x8jX7_9bMf5imy2mTl85OvUk6uP2_xNwht1pnlO420nOXXKvqe-RGvz1uv09GxQyfkXd06qg2y0VFf0xsXZ3TSf0d2EExNZ16oHFqvLAZOm9uWr-hmFM_opqOz_FaGD3xeZsVbYEqoOd4BJNajE8ekM8H-0fv3octMkNo8ogvwthENjdZrHOmY5vGmeXcsQpr60ltq9RfWXUp-GbCCZYY2BZVUthEJs7qNLHpQ7JZT-vqMaGlzkBtSFaVJeiPMpciBY8EnCxmojyySUDiFYuUacuWI3rGsfLbF5Grhq0K2Ko8W9VZQF6tvzltinZcOXoPOb8eiQW3_Q_T2Ui161dZq41EMFMhdWZyJxzureLIWeMiLbKAvES5UagWYHpGt7cbgEgssKV2eeSBwRIgaLszEpaz6XavREa16mSuLuQlIM_X3fglpsjV1XTZjJG4nZQBedQI6pokrKEPji7MkndEuENzt6eejH2xcYl49EwE5PVK2C-m9e__9MnVVDwlNxO_DFkYi22yuZgtq2fkuvm1mMxnPbLBh9y3oke29vYHxWHPx0qg_cjDnl_k2PIC2oJ9g1HFh37xFd4OP335DZ2CV-c |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFLZKQcCFfQkUMBKIA0RNnM1GQqgsVau2ox6K1JtxvMwMtJkyC6P5U_xG3nOSKSmitx64jcbOKM_zvcX2e-8j5EXKUm4KbcI0K1yY2jILeWlYmCvHRRE7HpnSk00UvR4_PBT7K-RXWwuDaZWtTfSG2ow0npGvgyPJkxz8e_b-5EeIrFF4u9pSaNSw2LGLOWzZJu-2P8H_-5Kxzc8HH7fChlUg1HlUTMNYRybXaazyTMUmiVNTFC6z2BdOKGMTX27pEogruOMZ0xDSW8ENE8wZlTCTwO9eIpchjGCRTxXcX95a4ClKW5jD8_UJ-FI8gGSY_MAzEc47zs9zBPztCf5whWfTNM_c1XoXuHnzf1u8W-RGE2zTjVo7bpMVW90hV_eadIK7pL8_xs-ITTpyVOnZ1NLvQ1PZBR1W3wBuFFPvqSdSp9ork6aTupL8LcWagT5VdLDAsjd67PNSLW2IOGDkqA-LMB0c3yNfLkTM-2S1GlX2IaGlSsEsisyWJdjHMhc8gYgLgshMR3lkWEDiFhJSN23ZkR3kSPrtGc9lDSMJMJIeRnIekNfLZ07qpiTnzv6ASFvOxIbi_ovRuC8b-ySNUVogWSsXKtW54w73jnHkjHaR4mlAXiFOJZo9eD2tmuoNEBIbiMmNIvLEZwwEWuvMBHOlu8MtRGVjLifyFJ8Beb4cxicxBbCyo1k9R-B2WQTkQa0YS5GQIwACeXjLoqMyHZm7I9Vw4Jupw2LBlp0H5E2rXKev9e81fXS-FM_Ita2DvV25u93beUyuM28CsjDma2R1Op7ZJ-SK_jkdTsZPvQGh5OtFK91vHZ-o-Q |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zb9QwELagoIoX7kKggJGQeICouezYvJVjBQJWKy71zXJ87KZHUmWzVP33eJxkacohId5W67HksWfGM_HMNwg9yZKM6VzpMCO5DTNTkJAVOgmptIznsWWRLnyziXw6ZXt7fHamit9nuw9Pkl1NA6A0Ve3OsbadijO6s3S3FHzaSyCtgBEenlxElzIXyUBS16fP39bvCPBdYyiV-e280XXkUft_tc1nLqfziZPnXk_9pTS59v_sXEdXe4cU73YSdANdMNVNtPmxf3K_heazBn7D-eHaYqlWrcEHpa7MKS6rfXckGNLTsW82jpUXOIWXXbX1Cwx59XMs8eIUSsPwkc_dNLhvVuFGDud1U7aLo9vo6-TNl1dvw747Q6holLdhrCJNVRZLSmSs0zjTeW6JAXw9LrVJfdmqTZ1_xiwjiXKhkeFMJzyxWqaJTrfQRlVX5i7Chcyc6eDEFIWzIQXlLHVeiXO0iIpopJMAxcMhCdVDl0MHjUPhQxhGRbeDwu2g8DsoTgL0bD3nuAPu-Cv1Szj7NSWAbvs_6mYueh0WWkvFoaEp4zJT1DIL8VUcWa1sJFkWoKcgOQJMg1uekn2Fg2MSQLbEbh755mCJY2h7ROlUWo2HB9kTvUlZCudJ0ZQ6B5cE6PF6GGZCmlxl6lVHwyGk5AG604nqmiXA0XfOrltlPhLiEc_jkapceMBxDj3pCQvQ80GUfy7rz3t679_IH6HN2euJ-PBu-v4-upJ4ZSBhzLbRRtuszAN0WX1vy2Xz0Gv4D2hbUJg |
| 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=Prediction+of+acute+kidney+injury+risk+after+cardiac+surgery%3A+using+a+hybrid+machine+learning+algorithm&rft.jtitle=BMC+medical+informatics+and+decision+making&rft.au=Petrosyan%2C+Yelena&rft.au=Mesana%2C+Thierry+G&rft.au=Sun%2C+Louise+Y&rft.date=2022-05-18&rft.pub=Springer+Nature+B.V&rft.eissn=1472-6947&rft.volume=22&rft.spage=1&rft_id=info:doi/10.1186%2Fs12911-022-01859-w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1472-6947&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1472-6947&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1472-6947&client=summon |