{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:54:46Z","timestamp":1757310886907,"version":"3.37.3"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 109-2410-H-166-001","MOST 107-2410-H-166-003"],"award-info":[{"award-number":["MOST 109-2410-H-166-001","MOST 107-2410-H-166-003"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007832","name":"Central Taiwan University of Science and Technology","doi-asserted-by":"publisher","award":["CTU108-P-019"],"award-info":[{"award-number":["CTU108-P-019"]}],"id":[{"id":"10.13039\/100007832","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s11227-021-03916-z","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T11:03:51Z","timestamp":1624532631000},"page":"2043-2071","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predictive models for detecting patients more likely to develop acute myocardial infarctions"],"prefix":"10.1007","volume":"78","author":[{"given":"Fu-Hsing","family":"Wu","sequence":"first","affiliation":[]},{"given":"Huey-Jen","family":"Lai","sequence":"additional","affiliation":[]},{"given":"Hsuan-Hung","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Po-Chou","family":"Chan","sequence":"additional","affiliation":[]},{"given":"Chien-Ming","family":"Tseng","sequence":"additional","affiliation":[]},{"given":"Kun-Min","family":"Chang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8966-1021","authenticated-orcid":false,"given":"Yung-Fu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chih-Sheng","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"3916_CR1","unstructured":"World Health Organization. Accessed from https:\/\/www.who.int\/cardiovascular_diseases\/about_cvd\/en\/"},{"key":"3916_CR2","unstructured":"Ministry of Health and Welfare of Taiwan. Accessed from https:\/\/www.mohw.gov.tw\/cp-16-48057-1.html"},{"key":"3916_CR3","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.disamonth.2012.12.004","volume":"59","author":"S Boateng","year":"2013","unstructured":"Boateng S, Sanborn T (2013) Acute myocardial infarction. Dis Mon 59:83\u201396","journal-title":"Dis Mon"},{"key":"3916_CR4","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1161\/CIRCULATIONAHA.106.633552","volume":"115","author":"F Lanas","year":"2007","unstructured":"Lanas F, Avezum A, Bautista LE, Diaz R, Luna M, Islam S et al (2007) Risk factors for acute myocardial infarction in Latin America: the INTERHEART Latin American study. Circulation 115:1067\u20131074","journal-title":"Circulation"},{"key":"3916_CR5","volume-title":"Recent advances in cardiovascular risk factors","author":"A Kumar","year":"2012","unstructured":"Kumar A (2012) Cardiovascular risk factors in elderly normolipidaemic acute myocardial infarct patients. In: Atiq M (ed) Recent advances in cardiovascular risk factors. IntechOpen, Croatia"},{"key":"3916_CR6","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.atherosclerosis.2019.08.024","volume":"289","author":"NM Isiozor","year":"2019","unstructured":"Isiozor NM, Kunutsor SK, Voutilainen A, Kurl S, Kauhanen J, Laukkanen JA (2019) Ideal cardiovascular health and risk of acute myocardial infarction among Finnish men. Atherosclerosis 289:126\u2013131","journal-title":"Atherosclerosis"},{"key":"3916_CR7","doi-asserted-by":"publisher","first-page":"1916","DOI":"10.1056\/NEJMoa021445","volume":"347","author":"Y Yamada","year":"2002","unstructured":"Yamada Y, Izawa H, Ichihara S, Takatsu F, Ishihara H, Hirayama H et al (2002) Prediction of the risk of myocardial infarction from polymorphisms in candidate genes. N Engl J Med 347:1916\u20131923","journal-title":"N Engl J Med"},{"key":"3916_CR8","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1001\/jama.293.10.1223","volume":"293","author":"AX Garg","year":"2005","unstructured":"Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux P, Beyene J et al (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293:1223\u20131238","journal-title":"JAMA"},{"key":"3916_CR9","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1177\/1555343415608973","volume":"10","author":"T Porat","year":"2016","unstructured":"Porat T, Kostopoulou O, Woolley A, Delaney BC (2016) Eliciting user decision requirements for designing computerized diagnostic support for family physicians. J Cognit Eng Decis Mak 10:57\u201373","journal-title":"J Cognit Eng Decis Mak"},{"key":"3916_CR10","doi-asserted-by":"publisher","first-page":"e0174708","DOI":"10.1371\/journal.pone.0174708","volume":"12","author":"S Horng","year":"2017","unstructured":"Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA (2017) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PloS one 12:e0174708","journal-title":"PloS one"},{"key":"3916_CR11","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1475-925X-12-S1-S4","volume":"12","author":"JC Hsu","year":"2013","unstructured":"Hsu JC, Chen YF, Chung WS, Tan TH, Chen TS, Chiang JY (2013) Clinical verification of a clinical decision support system for ventilator weaning. Biomed Eng Online 12:S4","journal-title":"Biomed Eng Online"},{"key":"3916_CR12","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.ijmedinf.2014.07.005","volume":"83","author":"G Luo","year":"2014","unstructured":"Luo G, Nkoy FL, Gesteland PH, Glasgow TS, Stone BL (2014) A systematic review of predictive modeling for bronchiolitis. Int J Med Informatics 83:691\u2013714","journal-title":"Int J Med Informatics"},{"key":"3916_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1093\/jamia\/ocw084","volume":"24","author":"K Dunn Lopez","year":"2017","unstructured":"Dunn Lopez K, Gephart SM, Raszewski R, Sousa V, Shehorn LE, Abraham J (2017) Integrative review of clinical decision support for registered nurses in acute care settings. J Am Med Inform Assoc 24:441\u2013450","journal-title":"J Am Med Inform Assoc"},{"key":"3916_CR14","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.artmed.2013.05.002","volume":"59","author":"A-MJ Scheepers-Hoeks","year":"2013","unstructured":"Scheepers-Hoeks A-MJ, Grouls RJ, Neef C, Ackerman EW, Korsten EH (2013) Physicians\u2019 responses to clinical decision support on an intensive care unit\u2014comparison of four different alerting methods. Artif Intell Med 59:33\u201338","journal-title":"Artif Intell Med"},{"key":"3916_CR15","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1177\/0009922816669097","volume":"56","author":"AK Otto","year":"2017","unstructured":"Otto AK, Dyer AA, Warren CM, Walkner M, Smith BM, Gupta RS (2017) The development of a clinical decision support system for the management of pediatric food allergy. Clin Pediatr 56:571\u2013578","journal-title":"Clin Pediatr"},{"key":"3916_CR16","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1197\/jamia.M2667","volume":"15","author":"E Ammenwerth","year":"2008","unstructured":"Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U (2008) The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc 15:585\u2013600","journal-title":"J Am Med Inform Assoc"},{"key":"3916_CR17","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10916-017-0717-4","volume":"41","author":"F Baypinar","year":"2017","unstructured":"Baypinar F, Kingma HJ, van der Hoeven RT, Becker ML (2017) Physicians\u2019 compliance with a clinical decision support system alerting during the prescribing process. J Med Syst 41:96","journal-title":"J Med Syst"},{"key":"3916_CR18","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/JBHI.2013.2250984","volume":"18","author":"YF Chen","year":"2013","unstructured":"Chen YF, Huang PC, Lin KC, Lin HH, Wang LE, Cheng CC et al (2013) Semi-automatic segmentation and classification of pap smear cells. IEEE J Biomed Health Inform 18:94\u2013108","journal-title":"IEEE J Biomed Health Inform"},{"key":"3916_CR19","doi-asserted-by":"publisher","first-page":"9621640","DOI":"10.1155\/2018\/9621640","volume":"2018","author":"YF Chen","year":"2018","unstructured":"Chen YF, Lin CS, Wang KA, Rahman LOA, Lee DJ, Chung WS et al (2018) Design of a clinical decision support system for fracture prediction using imbalanced dataset. J Healthcare Eng 2018:9621640","journal-title":"J Healthcare Eng"},{"key":"3916_CR20","unstructured":"Lai HJ, Lin HH, Tan TH, Lin CS, Chen YF. Designing a clinical decision support system to predict readmissions for patients admitted with all-cause conditions. J Ambient Intell Humaniz Comput, in press"},{"key":"3916_CR21","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/JBHI.2018.2877595","volume":"23","author":"YF Chen","year":"2018","unstructured":"Chen YF, Lin CS, Hong CF, Lee DJ, Sun C, Lin HH (2018) Design of a clinical decision support system for predicting erectile dysfunction in men using NHIRD dataset. IEEE J Biomed Health Inform 23:2127\u20132137","journal-title":"IEEE J Biomed Health Inform"},{"key":"3916_CR22","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.cmpb.2015.11.009","volume":"125","author":"H-H Rau","year":"2016","unstructured":"Rau H-H, Hsu C-Y, Lin Y-A, Atique S, Fuad A, Wei L-M et al (2016) Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network. Comput Methods Programs Biomed 125:58\u201365","journal-title":"Comput Methods Programs Biomed"},{"key":"3916_CR23","doi-asserted-by":"publisher","first-page":"928","DOI":"10.3390\/cancers13040928","volume":"13","author":"H-A Lee","year":"2021","unstructured":"Lee H-A, Chao LR, Hsu C-Y (2021) A 10-year probability deep neural network prediction model for lung cancer. Cancers 13:928","journal-title":"Cancers"},{"key":"3916_CR24","doi-asserted-by":"crossref","unstructured":"Chien K-L, Lin H-J, Su T-C, Chen Y-Y, Chen P-C (2018) Comparing the consistency and performance of various coronary heart disease prediction models for primary prevention using a national representative cohort in Taiwan. Circ J, pp. CJ-17\u20130910","DOI":"10.1253\/circj.CJ-17-0910"},{"key":"3916_CR25","doi-asserted-by":"publisher","first-page":"167605","DOI":"10.1109\/ACCESS.2019.2953920","volume":"7","author":"M Abdar","year":"2019","unstructured":"Abdar M, Acharya UR, Sarrafzadegan N, Makarenkov V (2019) NE-nu-SVC: A new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access 7:167605\u2013167620","journal-title":"IEEE Access"},{"key":"3916_CR26","doi-asserted-by":"publisher","first-page":"54007","DOI":"10.1109\/ACCESS.2019.2909969","volume":"7","author":"L Ali","year":"2019","unstructured":"Ali L, Niamat A, Khan JA, Golilarz NA, Xingzhong X, Noor A et al (2019) An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access 7:54007\u201354014","journal-title":"IEEE Access"},{"key":"3916_CR27","doi-asserted-by":"publisher","first-page":"14659","DOI":"10.1109\/ACCESS.2019.2962755","volume":"8","author":"A Gupta","year":"2019","unstructured":"Gupta A, Kumar R, Arora HS, Raman B (2019) MIFH: a machine intelligence framework for heart disease diagnosis. IEEE Access 8:14659\u201314674","journal-title":"IEEE Access"},{"key":"3916_CR28","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.patrec.2020.02.010","volume":"133","author":"E Nasarian","year":"2020","unstructured":"Nasarian E, Abdar M, Fahami MA, Alizadehsani R, Hussain S, Basiri ME et al (2020) Association between work-related features and coronary artery disease: A heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recogn Lett 133:33\u201340","journal-title":"Pattern Recogn Lett"},{"key":"3916_CR29","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1089\/tmj.2018.0076","volume":"25","author":"M Yahyaie","year":"2019","unstructured":"Yahyaie M, Tarokh MJ, Mahmoodyar MA (2019) Use of Internet of Things to provide a new model for remote heart attack prediction. Telemed e-Health 25:499\u2013510","journal-title":"Telemed e-Health"},{"key":"3916_CR30","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1161\/CIRCULATIONAHA.119.041980","volume":"140","author":"MP Than","year":"2019","unstructured":"Than MP, Pickering JW, Sandoval Y, Shah AS, Tsanas A, Apple FS et al (2019) Machine learning to predict the likelihood of acute myocardial infarction. Circulation 140:899\u2013909","journal-title":"Circulation"},{"key":"3916_CR31","doi-asserted-by":"crossref","unstructured":"Wu FH, Lin HH, Chan PC, Tseng CM, Chen YF, Lin CS (2020) Clinical decision support systems for predicting patients liable to acquire acute myocardial infarctions. In: International conference on pattern recognition and artificial intelligence, pp 622\u2013634","DOI":"10.1007\/978-3-030-59830-3_54"},{"key":"3916_CR32","doi-asserted-by":"publisher","first-page":"51","DOI":"10.5530\/jcdr.2015.2.2","volume":"6","author":"J Kojuri","year":"2015","unstructured":"Kojuri J, Boostani R, Dehghani P, Nowroozipour F, Saki N (2015) Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. J Cardiovasc Dis Res 6:51\u201359","journal-title":"J Cardiovasc Dis Res"},{"key":"3916_CR33","first-page":"110","volume":"5","author":"H Hamidi","year":"2016","unstructured":"Hamidi H, Daraie A (2016) A new hybrid method for improving the performance of myocardial infarction prediction. J Commun Health Res 5:110\u2013120","journal-title":"J Commun Health Res"},{"key":"3916_CR34","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1111\/ijcp.12629","volume":"69","author":"HH Lin","year":"2015","unstructured":"Lin HH, Ho FM, Chen YF, Tseng CM, Ho CC, Chung WS (2015) Increased risk of erectile dysfunction among patients with sleep disorders: a nationwide population-based cohort study. Int J Clin Pract 69:846\u2013852","journal-title":"Int J Clin Pract"},{"key":"3916_CR35","doi-asserted-by":"publisher","first-page":"1898","DOI":"10.3899\/jrheum.141105","volume":"42","author":"YF Chen","year":"2015","unstructured":"Chen YF, Lin HH, Lu CC, Hung CT, Lee MH, Hsu CY et al (2015) Gout and a subsequent increased risk of erectile dysfunction in men aged 64 and under: a nationwide cohort study in Taiwan. J Rheumatol 42:1898\u20131905","journal-title":"J Rheumatol"},{"key":"3916_CR36","doi-asserted-by":"publisher","first-page":"2996","DOI":"10.1001\/jama.294.23.2996","volume":"294","author":"IM Thompson","year":"2005","unstructured":"Thompson IM, Tangen CM, Goodman PJ, Probstfield JL, Moinpour CM, Coltman CA (2005) Erectile dysfunction and subsequent cardiovascular disease. JAMA 294:2996\u20133002","journal-title":"JAMA"},{"key":"3916_CR37","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/S0302-2838(03)00304-X","volume":"44","author":"T Speel","year":"2003","unstructured":"Speel T, Van Langen H, Meuleman E (2003) The risk of coronary heart disease in men with erectile dysfunction. Eur Urol 44:366\u2013371","journal-title":"Eur Urol"},{"key":"3916_CR38","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jacc.2007.09.033","volume":"51","author":"BJ Shen","year":"2008","unstructured":"Shen BJ, Avivi YE, Todaro JF, Spiro A, Laurenceau J-P, Ward KD et al (2008) Anxiety characteristics independently and prospectively predict myocardial infarction in men: the unique contribution of anxiety among psychologic factors. J Am Coll Cardiol 51:113\u2013119","journal-title":"J Am Coll Cardiol"},{"key":"3916_CR39","doi-asserted-by":"publisher","first-page":"2341","DOI":"10.1097\/01.ju.0000125198.32936.38","volume":"171","author":"AD Seftel","year":"2004","unstructured":"Seftel AD, Sun P, Swindle R (2004) The prevalence of hypertension, hyperlipidemia, diabetes mellitus and depression in men with erectile dysfunction. J Urol 171:2341\u20132345","journal-title":"J Urol"},{"key":"3916_CR40","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.jaci.2016.01.015","volume":"138","author":"YM Andersen","year":"2016","unstructured":"Andersen YM, Egeberg A, Gislason GH, Hansen PR, Skov L, Thyssen JP (2016) Risk of myocardial infarction, ischemic stroke, and cardiovascular death in patients with atopic dermatitis. J Allergy Clin Immunol 138:310\u2013312","journal-title":"J Allergy Clin Immunol"},{"key":"3916_CR41","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1111\/all.12685","volume":"70","author":"JI Silverberg","year":"2015","unstructured":"Silverberg JI (2015) Association between adult atopic dermatitis, cardiovascular disease, and increased heart attacks in three population-based studies. Allergy 70:1300\u20131308","journal-title":"Allergy"},{"key":"3916_CR42","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3109\/07853890.2013.870018","volume":"46","author":"VYF Su","year":"2014","unstructured":"Su VYF, Chen TJ, Yeh CM, Chou KT, Hung MH, Chu SY et al (2014) Atopic dermatitis and risk of ischemic stroke: a nationwide population-based study. Ann Med 46:84\u201389","journal-title":"Ann Med"},{"key":"3916_CR43","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.aat.2016.08.002","volume":"54","author":"CC Chang","year":"2016","unstructured":"Chang CC, Liao CC, Chen TL (2016) Perioperative medicine and Taiwan National Health Insurance Research Database. Acta Anaesthesiol Taiwan 54:93\u201396","journal-title":"Acta Anaesthesiol Taiwan"},{"key":"3916_CR44","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1023\/A:1012454411458","volume":"46","author":"D Decoste","year":"2002","unstructured":"Decoste D, Sch\u00f6lkopf B (2002) Training invariant support vector machines. Mach Learn 46:161\u2013190","journal-title":"Mach Learn"},{"key":"3916_CR45","unstructured":"LeCun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, et al. (1995) Comparison of learning algorithms for handwritten digit recognition. In: International Conference on Artificial Neural Networks, Perth, Australia, 1995, pp. 53\u201360"},{"key":"3916_CR46","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.patcog.2011.05.012","volume":"45","author":"K Lillywhite","year":"2012","unstructured":"Lillywhite K, Tippetts B, Lee DJ (2012) Self-tuned Evolution-COnstructed features for general object recognition. Pattern Recogn 45:241\u2013251","journal-title":"Pattern Recogn"},{"key":"3916_CR47","doi-asserted-by":"publisher","first-page":"13624","DOI":"10.1109\/ACCESS.2018.2810198","volume":"6","author":"P Tao","year":"2018","unstructured":"Tao P, Sun Z, Sun Z (2018) An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624\u201313631","journal-title":"IEEE Access"},{"key":"3916_CR48","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.asoc.2018.11.001","volume":"75","author":"Z Tao","year":"2019","unstructured":"Tao Z, Huiling L, Wenwen W, Xia Y (2019) GA-SVM based feature selection and parameter optimization in hospitalization expense modeling. Appl Soft Comput 75:323\u2013332","journal-title":"Appl Soft Comput"},{"key":"3916_CR49","doi-asserted-by":"publisher","first-page":"e0213007","DOI":"10.1371\/journal.pone.0213007","volume":"14","author":"C-Y Hung","year":"2019","unstructured":"Hung C-Y, Lin C-H, Lan T-H, Peng G-S, Lee C-C (2019) Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database. PloS one 14:e0213007","journal-title":"PloS one"},{"key":"3916_CR50","doi-asserted-by":"crossref","unstructured":"Kumar NM, Manjula R (2019) Design of multi-layer perceptron for the diagnosis of diabetes mellitus using Keras in deep learning. In: Smart intelligent computing and applications, ed: Springer, 2019, pp. 703\u2013711","DOI":"10.1007\/978-981-13-1921-1_68"},{"key":"3916_CR51","doi-asserted-by":"publisher","first-page":"1650025","DOI":"10.1142\/S0129065716500258","volume":"26","author":"A Ortiz","year":"2016","unstructured":"Ortiz A, Munilla J, Gorriz JM, Ramirez J (2016) Ensembles of deep learning architectures for the early diagnosis of the Alzheimer\u2019s disease. Int J Neural Syst 26:1650025","journal-title":"Int J Neural Syst"},{"key":"3916_CR52","doi-asserted-by":"publisher","first-page":"103986","DOI":"10.1016\/j.ijmedinf.2019.103986","volume":"132","author":"Y Ge","year":"2019","unstructured":"Ge Y, Wang Q, Wang L, Wu H, Peng C, Wang J et al (2019) Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inform 132:103986","journal-title":"Int J Med Inform"},{"key":"3916_CR53","doi-asserted-by":"publisher","first-page":"131094","DOI":"10.1109\/ACCESS.2019.2940644","volume":"7","author":"W Hong","year":"2019","unstructured":"Hong W, Xiong Z, Zheng N, Weng Y (2019) A medical-history-based potential disease prediction algorithm. IEEE Access 7:131094\u2013131101","journal-title":"IEEE Access"},{"key":"3916_CR54","unstructured":"Haq AU, Li J, Memon MH, Khan J, Din SU, Ahad I, et al. (2018) Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of Parkinson disease. In: 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 101\u2013106"},{"issue":"21","key":"3916_CR55","first-page":"2019","volume":"11","author":"SI Ayon","year":"2019","unstructured":"Ayon SI, Islam M (2019) Diabetes prediction: a deep learning approach. Int J Inf Eng Electron Bus 11(21):2019","journal-title":"Int J Inf Eng Electron Bus"},{"key":"3916_CR56","doi-asserted-by":"crossref","unstructured":"Kayal CK, Bagchi S, Dhar D, Maitra T, S. Chatterjee S (2019) Hepatocellular carcinoma survival prediction using deep neural network. In: Proceedings of International Ethical Hacking Conference 2018, pp. 349\u2013358","DOI":"10.1007\/978-981-13-1544-2_28"},{"key":"3916_CR57","doi-asserted-by":"publisher","first-page":"49","DOI":"10.51983\/ajcst-2019.8.2.2141","volume":"8","author":"M Ashraf","year":"2019","unstructured":"Ashraf M, Rizvi M, Sharma H (2019) Improved heart disease prediction using deep neural network. Asian J Comput Sci Technol 8:49\u201354","journal-title":"Asian J Comput Sci Technol"},{"key":"3916_CR58","doi-asserted-by":"crossref","unstructured":"Anderson A, Gregg D (2018) Optimal DNN primitive selection with partitioned boolean quadratic programming. In: Proceedings of the 2018 International Symposium on Code Generation and Optimization, 2018, pp. 340\u2013351","DOI":"10.1145\/3179541.3168805"},{"key":"3916_CR59","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1016\/j.procs.2018.05.154","volume":"132","author":"S Grover","year":"2018","unstructured":"Grover S, Bhartia S, Yadav A, Seeja K (2018) Predicting severity of Parkinson\u2019s disease using deep learning. Procedia Comput Sci 132:1788\u20131794","journal-title":"Procedia Comput Sci"},{"key":"3916_CR60","doi-asserted-by":"crossref","unstructured":"Goyal M (2017) Prediction of stroke using deep learning model. In: International Conference on Neural Information Processing, pp. 774\u2013781","DOI":"10.1007\/978-3-319-70139-4_78"},{"key":"3916_CR61","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30:1145\u20131159","journal-title":"Pattern Recogn"},{"key":"3916_CR62","unstructured":"Cortes C, Mohri M (2004) AUC optimization vs. error rate minimization. In: Advances in neural information processing systems, pp. 313\u2013320"},{"key":"3916_CR63","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1134\/S1990750813030128","volume":"7","author":"O Trifonova","year":"2013","unstructured":"Trifonova O, Lokhov P, Archakov A (2013) Metabolic profiling of human blood. Biochem Moscow Suppl Ser B: Biomed Chem 7:179\u2013186","journal-title":"Biochem Moscow Suppl Ser B: Biomed Chem"},{"key":"3916_CR64","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1111\/j.1365-2036.2006.02998.x","volume":"24","author":"E Cholongitas","year":"2006","unstructured":"Cholongitas E, Senzolo M, Patch D, Shaw S, Hui C, Burroughs A (2006) Scoring systems for assessing prognosis in critically ill adult cirrhotics. Aliment Pharmacol Ther 24:453\u2013464","journal-title":"Aliment Pharmacol Ther"},{"key":"3916_CR65","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1080\/17571472.2010.11493315","volume":"3","author":"TY Wu","year":"2010","unstructured":"Wu TY, Majeed A, Kuo KN (2010) An overview of the healthcare system in Taiwan. London J Prim Care 3:115\u2013119","journal-title":"London J Prim Care"},{"key":"3916_CR66","first-page":"1","volume":"2014","author":"MH Wu","year":"2014","unstructured":"Wu MH, Wu MJ, Chou LF, Chen TJ (2014) Patterns of nonemergent visits to different healthcare facilities on the same day: a nationwide analysis in Taiwan. Sci World J 2014:1\u20138","journal-title":"Sci World J"},{"key":"3916_CR67","doi-asserted-by":"publisher","first-page":"43","DOI":"10.4187\/respcare.05719","volume":"63","author":"U Hatipo\u011flu","year":"2018","unstructured":"Hatipo\u011flu U, Wells BJ, Chagin K, Joshi D, Milinovich A, Rothberg MB (2018) Predicting 30-day all-cause readmission risk for subjects admitted with pneumonia at the point of care. Respir Care 63:43\u201349","journal-title":"Respir Care"},{"key":"3916_CR68","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1093\/jamia\/ocv110","volume":"23","author":"X Cai","year":"2015","unstructured":"Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D et al (2015) Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc 23:553\u2013561","journal-title":"J Am Med Inform Assoc"},{"key":"3916_CR69","doi-asserted-by":"publisher","first-page":"e0181173","DOI":"10.1371\/journal.pone.0181173","volume":"12","author":"M Jamei","year":"2017","unstructured":"Jamei M, Nisnevich A, Wetchler E, Sudat S, Liu E (2017) Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PloS one 12:e0181173","journal-title":"PloS one"},{"key":"3916_CR70","doi-asserted-by":"publisher","first-page":"e003885","DOI":"10.1161\/CIRCOUTCOMES.117.003885","volume":"11","author":"LN Smith","year":"2018","unstructured":"Smith LN, Makam AN, Darden D, Mayo H, Das SR, Halm EA et al (2018) Acute myocardial infarction readmission risk prediction models: a systematic review of model performance. Circ Cardiovasc Qual Outcomes 11:e003885","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"3916_CR71","doi-asserted-by":"publisher","first-page":"104017","DOI":"10.1016\/j.ijmedinf.2019.104017","volume":"133","author":"M Muzny","year":"2019","unstructured":"Muzny M, Henriksen A, Giordanengo A, Muzik J, Gr\u00f8ttland A, Blixg\u00e5rd H et al (2019) Wearable sensors with possibilities for data exchange: analyzing status and needs of different actors in mobile health monitoring systems. Int J Med Inform 133:104017","journal-title":"Int J Med Inform"},{"key":"3916_CR72","doi-asserted-by":"publisher","first-page":"34717","DOI":"10.1109\/ACCESS.2020.2974687","volume":"8","author":"MA Khan","year":"2020","unstructured":"Khan MA (2020) An IoT framework for heart disease prediction based on MDCNN classifier. IEEE Access 8:34717\u201334727","journal-title":"IEEE Access"},{"key":"3916_CR73","unstructured":"Angelo R (2020) The internet of things (IoT), electronic health record (EHR), and federal legislation: the case for a national electronic personal health information (EPHI) record system. Issues Inform Syst vol. 21"},{"key":"3916_CR74","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H et al (2020) A comprehensive survey on transfer learning. Proc IEEE 109:43\u201376","journal-title":"Proc IEEE"},{"key":"3916_CR75","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s10115-017-1043-3","volume":"53","author":"KD Feuz","year":"2017","unstructured":"Feuz KD, Cook DJ (2017) Collegial activity learning between heterogeneous sensors. Knowl Inf Syst 53:337\u2013364","journal-title":"Knowl Inf Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03916-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-021-03916-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-021-03916-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,4]],"date-time":"2023-02-04T09:10:52Z","timestamp":1675501852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-021-03916-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":75,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["3916"],"URL":"https:\/\/doi.org\/10.1007\/s11227-021-03916-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"type":"print","value":"0920-8542"},{"type":"electronic","value":"1573-0484"}],"subject":[],"published":{"date-parts":[[2021,6,24]]},"assertion":[{"value":"24 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}