{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:37:33Z","timestamp":1782949053700,"version":"3.54.5"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>To develop and validate a machine learning tool within 48\u00a0h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48\u00a0h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT\/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC\u2009=\u20090.95) and external cohort (AUC\u2009=\u20090.873). The APCU was significant for biliogenic AP (OR\u2009=\u20094.25 [2.08\u20138.72], <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001), alcoholic AP (OR\u2009=\u20093.60 [1.67\u20137.72], <jats:italic>P<\/jats:italic>\u2009=\u20090.001), hyperlipidemic AP (OR\u2009=\u20092.63 [1.28\u20135.37], <jats:italic>P<\/jats:italic>\u2009=\u20090.008) and tumor AP (OR\u2009=\u20094.57 [2.14\u20139.72], <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). APCU yielded the highest clinical net benefit, comparatively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-02066-3","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T10:02:58Z","timestamp":1669716178000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning model identifies aggressive acute pancreatitis within 48\u00a0h of admission: a large retrospective study"],"prefix":"10.1186","volume":"22","author":[{"given":"Lei","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyao","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingxiao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"2066_CR1","first-page":"897107","volume":"2013","author":"P Pavlidis","year":"2013","unstructured":"Pavlidis P, Crichton S, Lemmich Smith J, Morrison D, Atkinson S, Wyncoll D, Ostermann M. Improved outcome of severe acute pancreatitis in the intensive care unit. Crit Care Res Pract. 2013;2013:897107.","journal-title":"Crit Care Res Pract"},{"key":"2066_CR2","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/S0140-6736(14)60649-8","volume":"386","author":"PG Lankisch","year":"2015","unstructured":"Lankisch PG, Apte M, Banks PA. Acute pancreatitis. Lancet. 2015;386:85\u201396.","journal-title":"Lancet"},{"key":"2066_CR3","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.pan.2020.03.001","volume":"20","author":"H Yasuda","year":"2020","unstructured":"Yasuda H, Horibe M, Sanui M, Sasaki M, Suzuki N, Sawano H, Goto T, Ikeura T, Takeda T, Oda T, et al. Etiology and mortality in severe acute pancreatitis: a multicenter study in Japan. Pancreatology. 2020;20:307\u201317.","journal-title":"Pancreatology"},{"key":"2066_CR4","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1017\/S0007114508123443","volume":"101","author":"MS Petrov","year":"2009","unstructured":"Petrov MS, Pylypchuk RD, Uchugina AF. A systematic review on the timing of artificial nutrition in acute pancreatitis. Br J Nutr. 2009;101:787\u201393.","journal-title":"Br J Nutr"},{"key":"2066_CR5","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/S0140-6736(85)92733-3","volume":"2","author":"AP Corfield","year":"1985","unstructured":"Corfield AP, Cooper MJ, Williamson RC, Mayer AD, McMahon MJ, Dickson AP, Shearer MG, Imrie CW. Prediction of severity in acute pancreatitis: prospective comparison of three prognostic indices. Lancet. 1985;2:403\u20137.","journal-title":"Lancet"},{"key":"2066_CR6","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1038\/ajg.2009.622","volume":"105","author":"GI Papachristou","year":"2010","unstructured":"Papachristou GI, Muddana V, Yadav D, O\u2019Connell M, Sanders MK, Slivka A, Whitcomb DC. Comparison of BISAP, Ranson\u2019s, APACHE-II, and CTSI scores in predicting organ failure, complications, and mortality in acute pancreatitis. Am J Gastroenterol. 2010;105:435\u201341 (quiz 442).","journal-title":"Am J Gastroenterol"},{"key":"2066_CR7","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1097\/MOG.0b013e32835955ef","volume":"28","author":"WA Walker","year":"2012","unstructured":"Walker WA. Current opinion in gastroenterology. Curr Opin Gastroenterol. 2012;28:547\u201350.","journal-title":"Curr Opin Gastroenterol"},{"key":"2066_CR8","doi-asserted-by":"publisher","first-page":"1916","DOI":"10.1212\/WNL.0b013e318259e221","volume":"78","author":"G Ntaios","year":"2012","unstructured":"Ntaios G, Faouzi M, Ferrari J, Lang W, Vemmos K, Michel P. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology. 2012;78:1916\u201322.","journal-title":"Neurology"},{"key":"2066_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-020-02297-w","volume":"18","author":"MY Ji","year":"2020","unstructured":"Ji MY, Yuan L, Lu SM, Gao MT, Zeng Z, Zhan N, Ding YJ, Liu ZR, Huang PX, Lu C, Dong WG. Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study. J Transl Med. 2020;18:1\u201312.","journal-title":"J Transl Med"},{"key":"2066_CR10","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1159\/000090032","volume":"6","author":"CB Pearce","year":"2006","unstructured":"Pearce CB, Gunn SR, Ahmed A, Johnson CD. Machine learning can improve prediction of severity in acute pancreatitis using admission values of APACHE II score and C-reactive protein. Pancreatology. 2006;6:123\u201331.","journal-title":"Pancreatology"},{"key":"2066_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12876-019-1016-y","volume":"19","author":"Q Qiu","year":"2019","unstructured":"Qiu Q, Nian YJ, Guo Y, Tang L, Lu N, Wen LZ, Wang B, Chen DF, Liu KJ. Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis. BMC Gastroenterol. 2019;19:1\u20139.","journal-title":"BMC Gastroenterol"},{"key":"2066_CR12","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/s11604-021-01098-5","volume":"39","author":"M Barat","year":"2021","unstructured":"Barat M, Chassagnon G, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Soyer P. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol. 2021;39:524\u20136.\u00a0","journal-title":"Jpn J Radiol"},{"key":"2066_CR13","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1111\/den.13875","volume":"33","author":"M Gorris","year":"2021","unstructured":"Gorris M, Hoogenboom SA, Wallace MB, van Hooft JE. Artificial intelligence for the management of pancreatic diseases. Dig Endosc. 2021;33:231\u201341.","journal-title":"Dig Endosc"},{"key":"2066_CR14","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.pan.2021.10.003","volume":"22","author":"R Thapa","year":"2022","unstructured":"Thapa R, Iqbal Z, Garikipati A, Siefkas A, Hoffman J, Mao QQ, Das R. Early prediction of severe acute pancreatitis using machine learning. Pancreatology. 2022;22:43\u201350.","journal-title":"Pancreatology"},{"key":"2066_CR15","doi-asserted-by":"publisher","first-page":"e2100264","DOI":"10.1002\/minf.202100264","volume":"41","author":"TNK Hung","year":"2022","unstructured":"Hung TNK, Le NQK, Le NH, Van Tuan L, Nguyen TP, Thi C, Kang JH. An AI-based prediction model for drug-drug interactions in osteoporosis and paget\u2019s diseases from SMILES. Mol Inform. 2022;41:e2100264.","journal-title":"Mol Inform"},{"key":"2066_CR16","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1016\/j.csbj.2022.04.021","volume":"20","author":"TH Vo","year":"2022","unstructured":"Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J. 2022;20:2112\u201323.","journal-title":"Comput Struct Biotechnol J"},{"key":"2066_CR17","doi-asserted-by":"publisher","first-page":"e011160","DOI":"10.1161\/JAHA.118.011160","volume":"8","author":"SJ Al'Aref","year":"2019","unstructured":"Al\u2019Aref SJ, Singh G, van Rosendael AR, Kolli KK, Ma X, Maliakal G, Pandey M, Lee BC, Wang J, Xu Z, et al. Determinants of in-hospital mortality after percutaneous coronary intervention: a machine learning approach. J Am Heart Assoc. 2019;8:e011160.","journal-title":"J Am Heart Assoc"},{"key":"2066_CR18","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1016\/j.eururo.2014.11.025","volume":"67","author":"GS Collins","year":"2015","unstructured":"Collins GS, Reitsma JB, Altman DG, Moons KGM, Grp T. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Eur Urol. 2015;67:1142\u201351.","journal-title":"Eur Urol"},{"key":"2066_CR19","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M14-0698","volume":"162","author":"KGM Moons","year":"2015","unstructured":"Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, Steyerberg EW, Vickers AJ, Ransohoff DF, Collins GS. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162:W1\u201373.","journal-title":"Ann Intern Med"},{"key":"2066_CR20","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1097\/00003246-199510000-00007","volume":"23","author":"JC Marshall","year":"1995","unstructured":"Marshall JC, Cook DJ, Christou NV, Bernard GR, Sprung CL, Sibbald WJ. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med. 1995;23:1638\u201352.","journal-title":"Crit Care Med"},{"key":"2066_CR21","doi-asserted-by":"crossref","unstructured":"Guidelines for intensive care unit admission, discharge, and triage. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine. Crit Care Med. 1999; 27:633\u20138.","DOI":"10.1097\/00003246-199903000-00048"},{"key":"2066_CR22","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1111\/jgh.12119","volume":"28","author":"W Qing","year":"2013","unstructured":"Qing W, Du TG. Clinical use of revised atlanta classification of acute pancreatitis in 2012. J Gastroenterol Hepatol. 2013;28:880\u2013880.","journal-title":"J Gastroenterol Hepatol"},{"key":"2066_CR23","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1038\/ajg.2008.66","volume":"104","author":"V Muddana","year":"2009","unstructured":"Muddana V, Whitcomb DC, Khalid A, Slivka A, Papachristou GI. Elevated serum creatinine as a marker of pancreatic necrosis in acute pancreatitis. Off J Am Coll Gastroenterol. 2009;104:164\u201370.","journal-title":"Off J Am Coll Gastroenterol"},{"key":"2066_CR24","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/S0002-9610(98)00296-7","volume":"177","author":"G Talamini","year":"1999","unstructured":"Talamini G, Uomo G, Pezzilli R, Rabitti PG, Billi P, Bassi C, Cavallini G, Pederzoli P. Serum creatinine and chest radiographs in the early assessment of acute pancreatitis. Am J Surg. 1999;177:7\u201314.","journal-title":"Am J Surg"},{"key":"2066_CR25","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1046\/j.1365-2168.2001.01673.x","volume":"88","author":"ML Kylanpaa-Back","year":"2001","unstructured":"Kylanpaa-Back ML, Takala A, Kemppainen E, Puolakkainen P, Haapiainen R, Repo H. Procalcitonin strip test in the early detection of severe acute pancreatitis. Br J Surg. 2001;88:222\u20137.","journal-title":"Br J Surg"},{"key":"2066_CR26","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1159\/000092104","volume":"6","author":"J Martinez","year":"2006","unstructured":"Martinez J, Johnson CD, Sanchez-Paya J, de Madaria E, Robles-Diaz G, Perez-Mateo M. Obesity is a definitive risk factor of severity and mortality in acute pancreatitis: an updated meta-analysis. Pancreatology. 2006;6:206\u20139.","journal-title":"Pancreatology"},{"key":"2066_CR27","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1111\/j.1467-9868.2011.01004.x","volume":"74","author":"R Tibshirani","year":"2012","unstructured":"Tibshirani R, Bien J, Friedman J, Hastie T, Simon N, Taylor J, Tibshirani RJ. Strong rules for discarding predictors in lasso-type problems. J R Stat Soc Ser B Stat Methodol. 2012;74:245\u201366.","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"2066_CR28","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: The 22nd ACM SIGKDD international conference. 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"2066_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-022-03369-9","volume":"20","author":"QQ Li","year":"2022","unstructured":"Li QQ, Yang H, Wang PP, Liu XC, Lv K, Ye MQ. XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer. J Transl Med. 2022;20:1\u201312.","journal-title":"J Transl Med"},{"key":"2066_CR30","doi-asserted-by":"publisher","first-page":"342","DOI":"10.21037\/tlcr-22-92","volume":"11","author":"D Lu","year":"2022","unstructured":"Lu D, Peng JX, Wang ZJ, Sun Y, Zhai JX, Wang ZZ, Chen ZM, Matsumoto Y, Wang L, Xin SX, Cai KC. Dielectric property measurements for the rapid differentiation of thoracic lymph nodes using XGBoost in patients with non-small cell lung cancer: a self-control clinical trial. Transl Lung Cancer Res. 2022;11:342\u201356.","journal-title":"Transl Lung Cancer Res"},{"key":"2066_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12967-020-02620-5","volume":"18","author":"NZ Hou","year":"2020","unstructured":"Hou NZ, Li MZ, He L, Xie B, Wang L, Zhang RM, Yu Y, Sun XD, Pan ZS, Wang K. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med. 2020;18:1\u201314.","journal-title":"J Transl Med"},{"key":"2066_CR32","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565\u201374.","journal-title":"Med Decis Making"},{"key":"2066_CR33","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1111\/j.1572-0241.2006.00856.x","volume":"101","author":"PA Banks","year":"2006","unstructured":"Banks PA, Freeman ML, Practice Parameters Committee of the American College of Gastroenterology. Practice guidelines in acute pancreatitis. Am J Gastroenterol. 2006;101:2379\u2013400.","journal-title":"Am J Gastroenterol"},{"key":"2066_CR34","first-page":"69","volume":"139","author":"JH Ranson","year":"1974","unstructured":"Ranson JH, Rifkind KM, Roses DF, Fink SD, Eng K, Spencer FC. Prognostic signs and the role of operative management in acute pancreatitis. Surg Gynecol Obstet. 1974;139:69\u201381.","journal-title":"Surg Gynecol Obstet"},{"key":"2066_CR35","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1097\/00003246-199206000-00025","volume":"20","author":"RC Bone","year":"1992","unstructured":"Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RMH, Sibbald WJ, Abrams JH, Bernard GR, et al. American-College of Chest Physicians\/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med. 1992;20:864\u201374.","journal-title":"Crit Care Med"},{"key":"2066_CR36","doi-asserted-by":"publisher","first-page":"e5310","DOI":"10.1136\/bmj.e5310","volume":"345","author":"A McGinley","year":"2012","unstructured":"McGinley A, Pearse RM. A national early warning score for acutely ill patients. BMJ. 2012;345:e5310.","journal-title":"BMJ"},{"key":"2066_CR37","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1136\/gut.2008.152702","volume":"57","author":"BU Wu","year":"2008","unstructured":"Wu BU, Johannes RS, Sun X, Tabak Y, Conwell DL, Banks PA. The early prediction of mortality in acute pancreatitis: a large population-based study. Gut. 2008;57:1698\u2013703.","journal-title":"Gut"},{"key":"2066_CR38","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1097\/00000658-197905000-00016","volume":"189","author":"JH Ranson","year":"1979","unstructured":"Ranson JH. The timing of biliary surgery in acute pancreatitis. Ann Surg. 1979;189:654.","journal-title":"Ann Surg"},{"key":"2066_CR39","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1097\/01.mpa.0000236727.16370.99","volume":"33","author":"CF Frey","year":"2006","unstructured":"Frey CF, Zhou H, Harvey DJ, White RH. The incidence and case-fatality rates of acute biliary alcoholic, and idiopathic pancreatitis in California, 1994\u20132001. Pancreas. 2006;33:336\u201344.","journal-title":"Pancreas"},{"key":"2066_CR40","doi-asserted-by":"publisher","first-page":"E1","DOI":"10.1016\/j.pan.2013.07.063","volume":"13","author":"M Besselink","year":"2013","unstructured":"Besselink M, van Santvoort H, Freeman M, Gardner T, Mayerle J, Vege SS, Werner J, Banks P, Mckay C, Fernandez-Del Castillo C, et al. IAP\/APA evidence-based guidelines for the management of acute pancreatitis. Pancreatology. 2013;13:E1\u201315.","journal-title":"Pancreatology"},{"key":"2066_CR41","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1038\/ajg.2013.218","volume":"108","author":"S Tenner","year":"2013","unstructured":"Tenner S, Baillie J, DeWitt J, Vege SS. American College of Gastroenterology guideline: management of acute pancreatitis. American J Gastroenterol. 2013;108:1400\u201315.","journal-title":"American J Gastroenterol"},{"key":"2066_CR42","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1053\/j.gastro.2018.01.032","volume":"154","author":"SD Crockett","year":"2018","unstructured":"Crockett SD, Wani S, Gardner TB, Falck-Ytter Y, Barkun AN, Ins AGA. American Gastroenterological Association Institute guideline on initial management of acute pancreatitis. Gastroenterology. 2018;154:1096\u2013101.","journal-title":"Gastroenterology"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-02066-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02066-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T06:41:24Z","timestamp":1669790484000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-02066-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["2066"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-02066-3","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,29]]},"assertion":[{"value":"28 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Renmin Hospital of Wuhan University (2021-RM-02106) and the Central Hospital of Wuhan (2021ks06109). Informed consent was waived by the Institutional Review Board of Renmin Hospital of Wuhan University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"312"}}