{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T10:45:06Z","timestamp":1774608306920,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"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>Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model\u2019s performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-02095-y","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T13:04:42Z","timestamp":1680786282000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System"],"prefix":"10.1186","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1070-8841","authenticated-orcid":false,"given":"Omid","family":"Mehrpour","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0117-872X","authenticated-orcid":false,"given":"Farhad","family":"Saeedi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3422-6799","authenticated-orcid":false,"given":"Samaneh","family":"Nakhaee","sequence":"additional","affiliation":[]},{"given":"Farbod","family":"Tavakkoli Khomeini","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Hadianfar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2495-5042","authenticated-orcid":false,"given":"Alireza","family":"Amirabadizadeh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4301-3795","authenticated-orcid":false,"given":"Christopher","family":"Hoyte","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"issue":"4","key":"2095_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1177\/0897190018815048","volume":"33","author":"AD Raval","year":"2020","unstructured":"Raval AD, Vyas A. National trends in diabetes medication use in the United States: 2008 to 2015. J Pharm Pract. 2020;33(4):433\u201342.","journal-title":"J Pharm Pract"},{"issue":"1","key":"2095_CR2","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1067\/mem.2001.114314","volume":"38","author":"RA Harrigan","year":"2001","unstructured":"Harrigan RA, Nathan MS, Beattie P. Oral agents for the treatment of type 2 diabetes mellitus: pharmacology, toxicity, and treatment. Ann Emerg Med. 2001;38(1):68\u201378.","journal-title":"Ann Emerg Med"},{"issue":"5","key":"2095_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.2165\/00002018-199819050-00007","volume":"19","author":"HA Spiller","year":"1998","unstructured":"Spiller HA. Management of antidiabetic medications in overdose. Drug Saf. 1998;19(5):411\u201324.","journal-title":"Drug Saf"},{"issue":"5","key":"2095_CR4","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1053\/ajem.2002.34955","volume":"20","author":"TL Litovitz","year":"2002","unstructured":"Litovitz TL, et al. 2001 Annual report of the American Association of Poison Control Centers Toxic Exposure Surveillance System. Am J Emerg Med. 2002;20(5):391\u2013452.","journal-title":"Am J Emerg Med"},{"issue":"5","key":"2095_CR5","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.ajem.2005.05.001","volume":"23","author":"WA Watson","year":"2005","unstructured":"Watson WA, et al. 2004 Annual report of the American Association of Poison Control Centers Toxic Exposure Surveillance System. Am J Emerg Med. 2005;23(5):589\u2013666.","journal-title":"Am J Emerg Med"},{"issue":"10","key":"2095_CR6","doi-asserted-by":"publisher","first-page":"929","DOI":"10.2146\/ajhp050500","volume":"63","author":"HA Spiller","year":"2006","unstructured":"Spiller HA, Sawyer TS. Toxicology of oral antidiabetic medications. Am J Health-System Pharm. 2006;63(10):929\u201338.","journal-title":"Am J Health-System Pharm"},{"issue":"10","key":"2095_CR7","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1080\/15563650.2016.1245421","volume":"54","author":"JB Mowry","year":"2016","unstructured":"Mowry JB, et al. 2015 Annual report of the American Association of Poison Control Centers\u2019 National Poison Data System (NPDS): 33rd annual report. Clin Toxicol (Phila). 2016;54(10):924\u20131109.","journal-title":"Clin Toxicol (Phila)"},{"issue":"10","key":"2095_CR8","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1080\/15563650802559632","volume":"46","author":"AC Bronstein","year":"2008","unstructured":"Bronstein AC, et al. 2007 annual report of the American Association of Poison Control Centers\u2019 National Poison Data System (NPDS): 25th annual report. Clin Toxicol. 2008;46(10):927\u20131057.","journal-title":"Clin Toxicol"},{"issue":"5","key":"2095_CR9","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1111\/bcpt.13273","volume":"125","author":"A Stevens","year":"2019","unstructured":"Stevens A, et al. Metformin overdose: a serious iatrogenic complication\u2014western France poison control centre data analysis. Basic Clin Pharmacol Toxicol. 2019;125(5):466\u201373.","journal-title":"Basic Clin Pharmacol Toxicol"},{"issue":"2","key":"2095_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s13181-010-0064-z","volume":"6","author":"PP Dougherty","year":"2010","unstructured":"Dougherty PP, Klein-Schwartz W. Octreotide\u2019s role in the management of sulfonylurea-induced hypoglycemia. J Med Toxicol. 2010;6(2):199\u2013206.","journal-title":"J Med Toxicol"},{"issue":"5","key":"2095_CR11","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1345\/aph.1D468","volume":"38","author":"HA Spiller","year":"2004","unstructured":"Spiller HA, Quadrani DA. Toxic effects from metformin exposure. Ann Pharmacother. 2004;38(5):776\u201380.","journal-title":"Ann Pharmacother"},{"issue":"4","key":"2095_CR12","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.2337\/diacare.26.4.1176","volume":"26","author":"GP Leese","year":"2003","unstructured":"Leese GP, et al. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes. A population-based study of health service resource use. Diabetes Care. 2003;26(4):1176\u201380.","journal-title":"Diabetes Care"},{"issue":"10","key":"2095_CR13","doi-asserted-by":"publisher","first-page":"929","DOI":"10.2146\/ajhp050500","volume":"63","author":"HA Spiller","year":"2006","unstructured":"Spiller HA, Sawyer TS. Toxicology of oral antidiabetic medications. Am J Health Syst Pharm. 2006;63(10):929\u201338.","journal-title":"Am J Health Syst Pharm"},{"issue":"2","key":"2095_CR14","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.emc.2007.02.010","volume":"25","author":"AK Rowden","year":"2007","unstructured":"Rowden AK, Fasano CJ. Emergency management of oral hypoglycemic drug toxicity. Emerg Med Clin N Am. 2007;25(2):347\u201356.","journal-title":"Emerg Med Clin N Am"},{"issue":"3","key":"2095_CR15","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1177\/0897190005277239","volume":"18","author":"P Lada","year":"2005","unstructured":"Lada P, Idrees U. Toxicity of oral agents used to treat diabetes. J Pharm Pract. 2005;18(3):145\u201356.","journal-title":"J Pharm Pract"},{"key":"2095_CR16","doi-asserted-by":"publisher","first-page":"e2014025","DOI":"10.4178\/epih\/e2014025","volume":"36","author":"J-M Bae","year":"2014","unstructured":"Bae J-M. The clinical decision analysis using decision tree. Epidemiol health. 2014;36:e2014025-5.","journal-title":"Epidemiol health"},{"issue":"1","key":"2095_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-019-0681-4","volume":"19","author":"JA Sidey-Gibbons","year":"2019","unstructured":"Sidey-Gibbons JA, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):1\u201318.","journal-title":"BMC Med Res Methodol"},{"issue":"5","key":"2095_CR18","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1023\/A:1016409317640","volume":"26","author":"V Podgorelec","year":"2002","unstructured":"Podgorelec V, et al. Decision trees: an overview and their use in medicine. J Med Syst. 2002;26(5):445\u201363.","journal-title":"J Med Syst"},{"issue":"2","key":"2095_CR19","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1086\/686020","volume":"6","author":"A Lungu","year":"2016","unstructured":"Lungu A, et al. Diagnosis of pulmonary hypertension from magnetic resonance imaging\u2013based computational models and decision tree analysis. Pulm Circ. 2016;6(2):181\u201390.","journal-title":"Pulm Circ"},{"issue":"1","key":"2095_CR20","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1111\/j.1349-7006.2006.00339.x","volume":"98","author":"Y Su","year":"2007","unstructured":"Su Y, et al. Diagnosis of gastric cancer using decision tree classification of mass spectral data. Cancer Sci. 2007;98(1):37\u201343.","journal-title":"Cancer Sci"},{"key":"2095_CR21","doi-asserted-by":"publisher","first-page":"105400","DOI":"10.1016\/j.cmpb.2020.105400","volume":"192","author":"MM Ghiasi","year":"2020","unstructured":"Ghiasi MM, Zendehboudi S, Mohsenipour AA. Decision tree-based diagnosis of coronary artery disease: CART model. Comput Methods Programs Biomed. 2020;192:105400.","journal-title":"Comput Methods Programs Biomed"},{"issue":"8","key":"2095_CR22","doi-asserted-by":"publisher","first-page":"3422","DOI":"10.1007\/s00330-018-5327-0","volume":"28","author":"YH Kim","year":"2018","unstructured":"Kim YH, et al. MRI-based decision tree model for diagnosis of biliary atresia. Eur Radiol. 2018;28(8):3422\u201331.","journal-title":"Eur Radiol"},{"issue":"6","key":"2095_CR23","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1080\/10826084.2017.1392981","volume":"53","author":"A Amirabadizadeh","year":"2018","unstructured":"Amirabadizadeh A, et al. Identifying risk factors for drug use in an Iranian treatment sample: a prediction approach using decision trees. Subst Use Misuse. 2018;53(6):1030\u201340.","journal-title":"Subst Use Misuse"},{"issue":"8","key":"2095_CR24","first-page":"15","volume":"163","author":"B Gupta","year":"2017","unstructured":"Gupta B, et al. Analysis of various decision tree algorithms for classification in data mining. Int J Comput Appl. 2017;163(8):15\u20139.","journal-title":"Int J Comput Appl"},{"issue":"1","key":"2095_CR25","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1006\/cbmr.1993.1005","volume":"26","author":"WJ Long","year":"1993","unstructured":"Long WJ, et al. A comparison of logistic regression to decision-tree induction in a medical domain. Comput Biomed Res. 1993;26(1):74\u201397.","journal-title":"Comput Biomed Res"},{"issue":"8","key":"2095_CR26","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1093\/comjnl\/41.8.537","volume":"41","author":"YH Li","year":"1998","unstructured":"Li YH, Jain AK. Classification of text documents. Comput J. 1998;41(8):537\u201346.","journal-title":"Comput J"},{"key":"2095_CR27","doi-asserted-by":"publisher","first-page":"104469","DOI":"10.1016\/j.compbiomed.2021.104469","volume":"134","author":"M Chary","year":"2021","unstructured":"Chary M, Boyer EW, Burns MM. Diagnosis of acute poisoning using explainable artificial intelligence. Comput Biol Med. 2021;134:104469.","journal-title":"Comput Biol Med"},{"issue":"6","key":"2095_CR28","first-page":"482","volume":"57","author":"M Chary","year":"2019","unstructured":"Chary M, Burnsa M, Boyerb E. Tak: the computational toxicological machine. 39th International Congress of the European Association of Poisons Centres and clinical toxicologists (EAPCCT) 21\u201324 May 2019, Naples, Italy. Clin Tox. 2019;57(6):482.","journal-title":"Clin Tox"},{"key":"2095_CR29","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/s13181-020-00769-5","volume":"16","author":"MA Chary","year":"2020","unstructured":"Chary MA, et al. The role and promise of artificial intelligence in medical toxicology. J Med Toxicol. 2020;16:458\u201364.","journal-title":"J Med Toxicol"},{"issue":"11","key":"2095_CR30","first-page":"1083","volume":"58","author":"D Nogee","year":"2020","unstructured":"Nogee D, et al. Multiclass classification machine learning identification of common poisonings. North American congress of clinical toxicology (NACCT) abstracts 2020. Clin Toxicol. 2020;58(11):1083\u20134.","journal-title":"Clin Toxicol"},{"issue":"10","key":"2095_CR31","first-page":"1049","volume":"56","author":"D Nogee","year":"2018","unstructured":"Nogee D, Tomassoni A. Development of a prototype software tool to assist with toxidrome recognition. North American congress of clinical toxicology (NACCT) abstracts 2018. Clin Toxicol. 2018;56(10):1049.","journal-title":"Clin Toxicol"},{"key":"2095_CR32","unstructured":"Sadeghian F, Saadat S, Goli S. The influences of cigarette smoking on psychomotor performance of driving: perceptual speed, 2-hand coordination. J Knowl Health Shahroud Univ Med Sci.\u00a02017;12(3)."},{"key":"2095_CR33","doi-asserted-by":"crossref","unstructured":"Benenson E, \u201cDiagnose to target\u201d in the setting of decision trees, In:\u00a0Syndrome-based approach to diagnosis: a practical guide, London: Springer-Verlag;\u00a0\u00a02013. pp.\u00a059\u2013106.","DOI":"10.1007\/978-1-4471-4733-6_8"},{"issue":"2","key":"2095_CR34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/jpm10020021","volume":"10","author":"G Battineni","year":"2010","unstructured":"Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of machine learning predictive models in the chronic disease diagnosis. J Pers Med. 2020:10(2):21. https:\/\/doi.org\/10.3390\/jpm10020021.","journal-title":"J Pers Med"},{"key":"2095_CR35","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.cmpb.2017.02.001","volume":"141","author":"M Tayefi","year":"2017","unstructured":"Tayefi M, et al. hs-CRP is strongly associated with coronary heart disease (CHD): a data mining approach using decision tree algorithm. Comput Methods Programs Biomed. 2017;141:105\u20139.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"2095_CR36","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.amepre.2004.10.011","volume":"28","author":"JS Kammerer","year":"2005","unstructured":"Kammerer JS, et al. Tuberculosis transmission in nontraditional settings: a decision-tree approach. Am J Prev Med. 2005;28(2):201\u20137.","journal-title":"Am J Prev Med"},{"issue":"2","key":"2095_CR37","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1080\/01480545.2020.1783286","volume":"45","author":"A Amirabadizadeh","year":"2020","unstructured":"Amirabadizadeh A, Nakhaee S, Mehrpour O. Risk assessment of elevated blood lead concentrations in the adult population using a decision tree approach. Drug Chem Toxicol. 2022;45(2):878\u201385","journal-title":"Drug Chem Toxicol"},{"issue":"6","key":"2095_CR38","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1080\/10826084.2017.1392981","volume":"53","author":"A Amirabadizadeh","year":"2018","unstructured":"Amirabadizadeh A, et al. Identifying risk factors for drug use in an Iranian treatment sample: a prediction approach using decision trees. Subst Use Misuse. 2018;53(6):1030\u201340.","journal-title":"Subst Use Misuse"},{"key":"2095_CR39","doi-asserted-by":"crossref","unstructured":"Zhang Z. Decision tree modeling using R. Ann Transl Med.\u00a02016;4(15).","DOI":"10.21037\/atm.2016.05.14"},{"key":"2095_CR40","doi-asserted-by":"crossref","unstructured":"Amirabadizadeh A, Nakhaee S, Mehrpour O. Risk assessment of elevated blood lead concentrations in the adult population using a decision tree approach. Drug Chem Toxicol.\u00a02020;1\u20138.","DOI":"10.1080\/01480545.2020.1783286"},{"issue":"11","key":"2095_CR41","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1016\/S2213-2600(18)30286-8","volume":"6","author":"SL Walsh","year":"2018","unstructured":"Walsh SL, et al. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837\u201345.","journal-title":"Lancet Respir Med"},{"issue":"1","key":"2095_CR42","doi-asserted-by":"publisher","first-page":"232","DOI":"10.7150\/thno.28447","volume":"9","author":"D-K Hwang","year":"2019","unstructured":"Hwang D-K, et al. Artificial intelligence-based decision-making for age-related macular degeneration. Theranostics. 2019;9(1):232.","journal-title":"Theranostics"},{"issue":"10","key":"2095_CR43","doi-asserted-by":"publisher","first-page":"e1913436","DOI":"10.1001\/jamanetworkopen.2019.13436","volume":"2","author":"M Phillips","year":"2019","unstructured":"Phillips M, et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open. 2019;2(10):e1913436-6.","journal-title":"JAMA Netw Open"},{"key":"2095_CR44","doi-asserted-by":"crossref","unstructured":"Topalovic M, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J.\u00a02019;53(4).","DOI":"10.1183\/13993003.01660-2018"},{"issue":"1","key":"2095_CR45","doi-asserted-by":"publisher","first-page":"34","DOI":"10.36548\/jaicn.2021.1.003","volume":"3","author":"V Balasubramaniam","year":"2021","unstructured":"Balasubramaniam V. Artificial intelligence algorithm with SVM classification using dermascopic images for melanoma diagnosis. J Artif Intell Capsul Netw. 2021;3(1):34\u201342.","journal-title":"J Artif Intell Capsul Netw"},{"issue":"3","key":"2095_CR46","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1007\/s13181-018-0667-3","volume":"14","author":"K Ouchi","year":"2018","unstructured":"Ouchi K, et al. Machine learning to predict, detect, and intervene older adults vulnerable for adverse drug events in the emergency department. J Med Toxicol. 2018;14(3):248\u201352.","journal-title":"J Med Toxicol"},{"issue":"6","key":"2095_CR47","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1080\/17476348.2020.1743181","volume":"14","author":"E Mekov","year":"2020","unstructured":"Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020;14(6):559\u201364.","journal-title":"Expert Rev Respir Med"},{"issue":"10","key":"2095_CR48","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.2337\/diacare.21.10.1659","volume":"21","author":"JB Brown","year":"1998","unstructured":"Brown JB, et al. Lactic acidosis rates in type 2 diabetes. Diabetes Care. 1998;21(10):1659\u201363.","journal-title":"Diabetes Care"},{"issue":"1","key":"2095_CR49","doi-asserted-by":"publisher","first-page":"124","DOI":"10.3810\/pgm.2012.01.2525","volume":"124","author":"M Bron","year":"2012","unstructured":"Bron M, et al. Hypoglycemia, treatment discontinuation, and costs in patients with type 2 diabetes mellitus on oral antidiabetic drugs. Postgrad Med. 2012;124(1):124\u201332.","journal-title":"Postgrad Med"},{"issue":"6","key":"2095_CR50","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/S0736-4679(98)00103-6","volume":"16","author":"SC Kwong","year":"1998","unstructured":"Kwong SC, Brubacher J. Phenformin and lactic acidosis: a case report and review. J Emerg Med. 1998;16(6):881\u20136.","journal-title":"J Emerg Med"},{"issue":"6","key":"2095_CR51","doi-asserted-by":"publisher","first-page":"755","DOI":"10.2337\/diacare.15.6.755","volume":"15","author":"CJ Bailey","year":"1992","unstructured":"Bailey CJ. Biguanides and NIDDM. Diabetes Care. 1992;15(6):755\u201372.","journal-title":"Diabetes Care"},{"issue":"12","key":"2095_CR52","doi-asserted-by":"publisher","first-page":"2097","DOI":"10.1002\/ccr3.1255","volume":"5","author":"N Barrella","year":"2017","unstructured":"Barrella N, Eisenberg B, Simpson SN. Hypoglycemia and severe lactic acidosis in a dog following metformin exposure. Clin case Rep. 2017;5(12):2097\u2013104.","journal-title":"Clin case Rep"},{"issue":"1","key":"2095_CR53","doi-asserted-by":"publisher","first-page":"5116","DOI":"10.1038\/ncomms6116","volume":"5","author":"J Broichhagen","year":"2014","unstructured":"Broichhagen J, et al. Optical control of insulin release using a photoswitchable sulfonylurea. Nat Commun. 2014;5(1):5116.","journal-title":"Nat Commun"},{"key":"2095_CR54","doi-asserted-by":"crossref","unstructured":"Seidowsky A, et al. Metformin-associated lactic acidosis: a prognostic and therapeutic study. Crit Care Med.\u00a02009. 37(7).","DOI":"10.1097\/CCM.0b013e3181a02490"},{"issue":"6","key":"2095_CR55","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1111\/jdi.12328","volume":"6","author":"KY Hur","year":"2015","unstructured":"Hur KY, Lee M-S. New mechanisms of metformin action: focusing on mitochondria and the gut. J Diabetes Invest. 2015;6(6):600\u20139.","journal-title":"J Diabetes Invest"},{"issue":"1","key":"2095_CR56","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0735-6757(97)90073-5","volume":"15","author":"WJ Brady","year":"1997","unstructured":"Brady WJ, Carter CT. Metformin overdose. Am J Emerg Med. 1997;15(1):107\u20138.","journal-title":"Am J Emerg Med"},{"issue":"5725","key":"2095_CR57","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1136\/bmj.3.5725.752","volume":"3","author":"JP Bingle","year":"1970","unstructured":"Bingle JP, Storey GW, Winter JM. Fatal self-poisoning with phenformin. BMJ. 1970;3(5725):752\u20132.","journal-title":"BMJ"},{"issue":"2","key":"2095_CR58","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1093\/bja\/ael347","volume":"98","author":"M Galea","year":"2007","unstructured":"Galea M, et al. Severe lactic acidosis and rhabdomyolysis following metformin and ramipril overdose. Br J Anaesth. 2007;98(2):213\u20135.","journal-title":"Br J Anaesth"},{"issue":"8","key":"2095_CR59","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1097\/PEC.0000000000000517","volume":"31","author":"VS Bebarta","year":"2015","unstructured":"Bebarta VS, Pead J, Varney SM. Lacticemia after acute overdose of metformin in an adolescent managed without intravenous sodium bicarbonate or extracorporeal therapy. Pediatr Emerg Care. 2015;31(8):589\u201390.","journal-title":"Pediatr Emerg Care"},{"key":"2095_CR60","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.4066\/biomedicalresearch.40-18-526","volume":"29","author":"M Fatima","year":"2018","unstructured":"Fatima M, Sadeeqa S, Nazir S. Metformin and its gastrointestinal problems: a review. Biomed Res. 2018;29:2285\u20139.","journal-title":"Biomed Res"},{"issue":"4","key":"2095_CR61","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1111\/dom.12854","volume":"19","author":"F Bonnet","year":"2017","unstructured":"Bonnet F, Scheen A. Underst overcoming metformin gastrointest intolerance. Diabetes Obes Metab. 2017;19(4):473\u201381.","journal-title":"Diabetes Obes Metab"},{"key":"2095_CR62","unstructured":"Colorado Multiple Institutional Review Board (COMIRB). Available at: https:\/\/research.cuanschutz.edu\/docs\/librariesprovider148\/comirb_documents\/guidance\/comirb-policy-and-procedures-document.pdf?fvrsn=9f172fb9_20. Accessed\u00a0 27 Mar\u00a02022."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02095-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-02095-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02095-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T14:05:54Z","timestamp":1680789954000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-02095-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,6]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2095"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-02095-y","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,6]]},"assertion":[{"value":"30 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All methods were carried out in accordance with relevant guidelines and regulations. Based on the guidelines and procedures of the Colorado Multiple Institutional Review Board on Human Subjects Protection the analysis of the NPDS data for this research study does not meet the criteria for human subjects according to the 45 Code of Federal Regulations (CFR) 46.101(b) and no approval of the institutional review board was required. Certain categories of research, exempt research, are not subject to federal regulations and do not require convened Institutional Review Board (IRB) review and approval. Research activities that meet the criteria set forth by the federal regulations 45 CFR 46.101(b) and that involve minimal risk may qualify for exemption [, ]. This study was reviewed by Colorado Multiple Institutional Review Board on Human Subjects Protection and determined to be exempt (COMIRB#: 22-1088).","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 conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"60"}}