{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T16:17:25Z","timestamp":1791994645787,"version":"4.4.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This study applied machine learning (ML) algorithms to construct a model for predicting EN initiation for patients in the intensive care unit (ICU) and identifying populations in need of EN at an early stage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study collected patient information from the Medical Information Mart for Intensive Care IV database. All patients enrolled were split randomly into a training set and a validation set. Six ML models were established to evaluate the initiation of EN, and the best model was determined according to the area under curve (AUC) and accuracy. The best model was interpreted using the Local Interpretable Model-Agnostic Explanations (LIME) algorithm and SHapley Additive exPlanation (SHAP) values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 53,150 patients participated in the study. They were divided into a training set (42,520, 80%) and a validation set (10,630, 20%). In the validation set, XGBoost had the optimal prediction performance with an AUC of 0.895. The SHAP values revealed that sepsis, sequential organ failure assessment score, and acute kidney injury were the three most important factors affecting EN initiation. The individualized forecasts were displayed using the LIME algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The XGBoost model was established and validated for early prediction of EN initiation in ICU patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2023.1060398","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:36:37Z","timestamp":1681432597000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients"],"prefix":"10.3389","volume":"10","author":[{"given":"Ya-Xi","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun-Liang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Hui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai-Na","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Min","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu-Feng","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1111\/1747-0080.12767","article-title":"Association of Subjective Global Assessment with outcomes in the intensive care unit: a retrospective cohort study","volume":"79","author":"Ferrie","year":"2022","journal-title":"Nutr Dietet"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.clnu.2016.09.004","article-title":"ESPEN guidelines on definitions and terminology of clinical nutrition","volume":"36","author":"Cederholm","year":"2017","journal-title":"Clin Nutr"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1007\/s00134-016-4665-0","article-title":"Early enteral nutrition in critically ill patients: ESICM clinical practice guidelines","volume":"43","author":"Reintam Blaser","year":"2017","journal-title":"Intensive Care Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.clnu.2017.12.008","article-title":"Enteral nutrition improves clinical outcome and reduces costs of acute mesenteric ischaemia after recanalisation in the intensive care unit","volume":"38","author":"Yang","year":"2019","journal-title":"Clin Nutr"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"392","DOI":"10.4103\/ijnmr.IJNMR_421_20","article-title":"Early enteral nutrition met calories goals led by nurse on improve clinical outcome: a systematic scoping review","volume":"26","author":"Koontalay","year":"2021","journal-title":"Iran J Nurs Midwifery Res"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1177\/0148607115621863","article-title":"Guidelines for the provision and assessment of nutrition support therapy in the adult critically ill patient: society of critical care medicine (SCCM) and American society for parenteral and enteral nutrition (a.S.P.E.N.)","volume":"40","author":"McClave","year":"2016","journal-title":"JPEN J Parenter Enteral Nutr"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.clnu.2016.07.012","article-title":"NutritionDay ICU: a 7 year worldwide prevalence study of nutrition practice in intensive care","volume":"36","author":"Bendavid","year":"2017","journal-title":"Clin Nutr"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat Methods"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1093\/ajcn\/nqaa027","article-title":"Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes","volume":"111","author":"Bodnar","year":"2020","journal-title":"Am J Clin Nutr"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e032703","DOI":"10.1136\/bmjopen-2019-032703","article-title":"Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data","volume":"9","author":"Rigdon","year":"2019","journal-title":"BMJ Open"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1016\/j.csbj.2022.06.003","article-title":"Application of interpretable machine learning for early prediction of prognosis in acute kidney injury","volume":"20","author":"Hu","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"Johnson","year":"2016","journal-title":"Sci Data"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/bjs.9736","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"102","author":"Collins","year":"2015","journal-title":"Br J Surg"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","article-title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data","volume":"43","author":"Quan","year":"2005","journal-title":"Med Care"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3978\/j.issn.2305-5839.2015.12.63","article-title":"Multiple imputation with multivariate imputation by chained equation (MICE) package","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann Transl Med"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.clnu.2018.08.037","article-title":"ESPEN guideline on clinical nutrition in the intensive care unit","volume":"38","author":"Singer","year":"2019","journal-title":"Clin Nutr"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","article-title":"Interpretation of compound activity predictions from complex machine learning models using local approximations and Shapley values","volume":"63","author":"Rodr\u00edguez-P\u00e9rez","year":"2020","journal-title":"J Med Chem"},{"key":"ref18","first-page":"1135","article-title":"\u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier.","volume-title":"In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201816)","author":"Ribeiro","year":"2016"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.2215\/CJN.17311120","article-title":"Predictive approaches for acute dialysis requirement and death in COVID-19","volume":"16","author":"Vaid","year":"2021","journal-title":"Clin J Am Soc Nephrol"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.clnesp.2021.08.022","article-title":"Explainable machine learning model to predict refeeding hypophosphatemia","volume":"45","author":"Choi","year":"2021","journal-title":"Clin Nutr ESPEN"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1056\/NEJMoa1102662","article-title":"Early versus late parenteral nutrition in critically ill adults","volume":"365","author":"Casaer","year":"2011","journal-title":"N Engl J Med"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s12911-022-01817-6","article-title":"Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in Central Taiwan","volume":"22","author":"Chan","year":"2022","journal-title":"BMC Med Inform Decis Mak"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"4900","DOI":"10.3748\/wjg.v27.i29.4900","article-title":"Effects of permissive hypocaloric vs standard enteral feeding on gastrointestinal function and outcomes in sepsis","volume":"27","author":"Sun","year":"2021","journal-title":"World J Gastroenterol"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.clnu.2020.05.011","article-title":"Association between nutrition support and acute gastrointestinal injury in critically ill patients during the first 72 hours","volume":"40","author":"Li","year":"2021","journal-title":"Clin Nutr"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1016\/j.clnu.2021.01.028","article-title":"ESPEN guideline on clinical nutrition in hospitalized patients with acute or chronic kidney disease","volume":"40","author":"Fiaccadori","year":"2021","journal-title":"Clin Nutr"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3969\/j.issn.2095-5227.2021.06.005","article-title":"Factors associated with success initiation of enteral nutrition in severe acute pancreatitis","volume":"42","author":"Zhao","year":"2021","journal-title":"Acad J Chin PLA Med Sch"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.clnu.2005.06.013","article-title":"ESPEN guidelines on artificial enteral nutrition--percutaneous endoscopic gastrostomy (PEG)","volume":"24","author":"L\u00f6ser","year":"2005","journal-title":"Clin Nutr"}],"container-title":["Frontiers in Nutrition"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2023.1060398\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:36:43Z","timestamp":1681432603000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnut.2023.1060398\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,14]]},"references-count":27,"alternative-id":["10.3389\/fnut.2023.1060398"],"URL":"https:\/\/doi.org\/10.3389\/fnut.2023.1060398","relation":{},"ISSN":["2296-861X"],"issn-type":[{"value":"2296-861X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,14]]},"article-number":"1060398"}}