{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T19:16:39Z","timestamp":1782760599687,"version":"3.54.5"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72042013"],"award-info":[{"award-number":["72042013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of infectious diseases so as to assist in clinical infectious-disease decision-making.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious diseases is constructed. The data includes 20,620 cases covering seven types of infectious diseases, including outpatients and inpatients, of which training data accounted for 80%, i.e., 16,496 cases, and test data accounted for 20%, i.e., 4124 cases. Through the auto-encoder, data normalization and sparse data densification processing are carried out to improve the model training effect. A residual network and attention mechanism are introduced into the MIDDM model to improve the performance of the model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Result<\/jats:title>\n                <jats:p>MIDDM achieved improved prediction results in diagnosing seven kinds of infectious diseases. In the case of similar disease diagnosis characteristics and similar interference factors, the prediction accuracy of disease classification with more sample data is significantly higher than the prediction accuracy of disease classification with fewer sample data. For instance, the training data for viral hepatitis, influenza, and hand foot and mouth disease were 2954, 3924, and 3015 respectively and the corresponding test accuracy rates were 99.86%, 98.47%, and 97.31%. There is less training data for syphilis, infectious diarrhea, and measles, i.e., 1208, 575, and 190 respectively and the corresponding test accuracy rates were noticeably lower, i.e., 83.03%, 87.30%, and42.11%. We also compared the MIDDM model with the models used in other studies. Using the same input data, taking viral hepatitis as an example, the accuracy of MIDDM is 99.44%, which is significantly higher than that of XGBoost (96.19%), Decision tree (90.13%), Bayesian method (85.19%), and logistic regression (91.26%). Other diseases were also significantly better predicted by MIDDM than by these three models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The application of the MIDDM model to multi-class diagnosis and prediction of infectious diseases can improve the accuracy of infectious-disease diagnosis. However, these results need to be further confirmed via clinical randomized controlled trials.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01776-y","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T04:31:01Z","timestamp":1644985861000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records"],"prefix":"10.1186","volume":"22","author":[{"given":"Mengying","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mo","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianzhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"issue":"9649","key":"1776_CR1","doi-asserted-by":"publisher","first-page":"1598","DOI":"10.1016\/S0140-6736(08)61365-3","volume":"372","author":"L Wang","year":"2008","unstructured":"Wang L, Wang Y, Jin S, et al. Emergence and control of infectious diseases in China. Lancet. 2008;372(9649):1598\u2013605.","journal-title":"Lancet"},{"key":"1776_CR2","unstructured":"China CDC, \u201cInfectious Diseases\u201d, http:\/\/www.chinacdc.cn\/jkzt\/crb\/."},{"key":"1776_CR3","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/S1473-3099(17)30227-X","volume":"17","author":"S Yang","year":"2017","unstructured":"Yang S, Wu J, Ding C, et al. Epidemiological features of and changes in incidence of infectious diseases in China in the first decade after the SARS outbreak: an observational trend study. Lancet Infectious Diseases. 2017;17:716\u201325.","journal-title":"Lancet Infectious Diseases"},{"issue":"1","key":"1776_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","volume":"25","author":"AY Hannun","year":"2019","unstructured":"Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med. 2019;25(1):65\u20139.","journal-title":"Nat Med"},{"issue":"1","key":"1776_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41591-018-0240-2","volume":"25","author":"ZI Attia","year":"2019","unstructured":"Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70\u20134.","journal-title":"Nat Med"},{"issue":"1","key":"1776_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1148\/radiol.2019182128","volume":"292","author":"B Wildman-Tobriner","year":"2019","unstructured":"Wildman-Tobriner B, Buda M, Hoang JK, Middleton WD, Thayer D, Short RG, et al. Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility. Radiology. 2019;292(1):112\u20139.","journal-title":"Radiology"},{"issue":"6","key":"1776_CR7","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1109\/JBHI.2020.3037079","volume":"25","author":"Y Li","year":"2020","unstructured":"Li Y, Li Y, Tian H. Deep learning-based end-to-end diagnosis system for avascular necrosis of femoral head. IEEE J Biomed Health Inf. 2020;25(6):2093\u201321.","journal-title":"IEEE J Biomed Health Inf"},{"issue":"8","key":"1776_CR8","doi-asserted-by":"publisher","first-page":"e04614","DOI":"10.1016\/j.heliyon.2020.e04614","volume":"6","author":"S Sathitratanacheewin","year":"2020","unstructured":"Sathitratanacheewin S, Sunanta P, Pongpirul K. Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon. 2020;6(8):e04614.","journal-title":"Heliyon"},{"issue":"22","key":"1776_CR9","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"B Ehteshami-Bejnordi","year":"2017","unstructured":"Ehteshami-Bejnordi B, Veta M, van Diest PJ, van Ginneken B, Karssemeijer N, Litjens G, the CAMELYON16 Consortium, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199\u2013210.","journal-title":"JAMA"},{"issue":"7639","key":"1776_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115\u20138.","journal-title":"Nature"},{"issue":"22","key":"1776_CR11","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402\u201310.","journal-title":"JAMA"},{"issue":"4","key":"1776_CR12","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2015","unstructured":"Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease. IEEE Trans Biomed Eng. 2015;62(4):1132\u201340.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"1776_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.artmed.2011.04.001","volume":"52","author":"DP Rogachev","year":"2011","unstructured":"Rogachev DP. Classification of infectious diseases based on chemiluminescent signatures of phagocytes in whole blood. Artif Intell Med. 2011;52(3):153\u201363.","journal-title":"Artif Intell Med"},{"issue":"67","key":"1776_CR14","first-page":"208","volume":"2020","author":"S Wang","year":"2020","unstructured":"Wang S, Govindaraj VV. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Int J Inf Fus. 2020;2020(67):208\u201329.","journal-title":"Int J Inf Fus"},{"issue":"1","key":"1776_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/s41746-020-00322-2","volume":"3","author":"P Rajpurkar","year":"2020","unstructured":"Rajpurkar P, O\u2019Connell C, Schechter A, et al. CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. npj Digital Med. 2020;3(1):115.","journal-title":"npj Digital Med"},{"key":"1776_CR16","doi-asserted-by":"publisher","first-page":"103398","DOI":"10.1016\/j.compbiomed.2019.103398","volume":"113","author":"B Msta","year":"2019","unstructured":"Msta B, Kspc D, Hah E, et al. CCMapper: an adaptive NLP-based free-text chief complaint mapping algorithm. Comput Biol Med. 2019;113:103398.","journal-title":"Comput Biol Med"},{"key":"1776_CR17","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.eswa.2016.10.065","volume":"72","author":"X Wang","year":"2017","unstructured":"Wang X, et al. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Syst Appl. 2017;72:221\u201330.","journal-title":"Expert Syst Appl"},{"key":"1776_CR18","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.compbiomed.2019.04.002","volume":"108","author":"K Xu","year":"2019","unstructured":"Xu K, Yang Z, Kang P, et al. Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition. Comput Biol Med. 2019;108:122\u201332.","journal-title":"Comput Biol Med"},{"key":"1776_CR19","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-319-50496-4","volume-title":"A convolution BiLSTM neural network model for Chinese event extraction (Chapter 23)","author":"CY Lin","year":"2016","unstructured":"Lin CY, Xue N, Zhao D, et al. A convolution BiLSTM neural network model for Chinese event extraction (Chapter 23). Berlin: Springer; 2016. p. 275\u201387. https:\/\/doi.org\/10.1007\/978-3-319-50496-4."},{"key":"1776_CR20","doi-asserted-by":"crossref","unstructured":"Li M, Zhang Y, Huang M, et al. Named entity recognition in Chinese electronic medical record using attention mechanism. In: 2019 international conference on internet of things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2019.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00125"},{"issue":"002","key":"1776_CR21","first-page":"133","volume":"034","author":"P Li","year":"2019","unstructured":"Li P, Yuan Z, Wenbo Tu. medical knowledge extraction and analysis from electronic medical records using deep learning. Chin J Med Sci. 2019;034(002):133\u20139.","journal-title":"Chin J Med Sci"},{"issue":"5\u20136","key":"1776_CR22","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/S1532-0464(03)00034-0","volume":"35","author":"S Dreiseitl","year":"2002","unstructured":"Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform. 2002;35(5\u20136):352\u20139.","journal-title":"J Biomed Inform"},{"key":"1776_CR23","volume-title":"Logistic regression for disease classification using microarray data","author":"JG Liao","year":"2007","unstructured":"Liao JG, Chin KV. Logistic regression for disease classification using microarray data. Oxford: Oxford University Press; 2007."},{"issue":"7","key":"1776_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1355-9","volume":"43","author":"JR Rajan","year":"2019","unstructured":"Rajan JR, Chelvan AC, Duela JS. Multi-class neural networks to predict lung cancer. J Med Syst. 2019;43(7):1\u20136.","journal-title":"J Med Syst"},{"issue":"cs1","key":"1776_CR25","first-page":"293","volume":"6","author":"S Elango","year":"2016","unstructured":"Elango S, Sundararajan J. MNN: multiclass neural network classifier for cardiac disease prediction models. Asian J Res Soc Sci Human. 2016;6(cs1):293.","journal-title":"Asian J Res Soc Sci Human"},{"issue":"1","key":"1776_CR26","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/JBHI.2018.2856820","volume":"23","author":"C Zhou","year":"2019","unstructured":"Zhou C, Jia Y, Motani M. Optimizing autoencoders for learning deep representations from health data. IEEE J Biomed Health Inf. 2019;23(1):103\u201311.","journal-title":"IEEE J Biomed Health Inf"},{"key":"1776_CR27","unstructured":"Im, D.J.; Ahn, S.; Memisevic, R.; Bengio, Y. Auto-encoding variational bayes."},{"issue":"C","key":"1776_CR28","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","volume":"15","author":"I Kavakiotis","year":"2017","unstructured":"Kavakiotis I, Tsave O, Salifoglou A, et al. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15(C):104\u201316.","journal-title":"Comput Struct Biotechnol J"},{"key":"1776_CR29","unstructured":"Kingma DP, Welling M. Auto-encoding variational Bayes.\u00a02013. https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"1776_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aei.2019.04.007","volume":"41","author":"N Jung","year":"2019","unstructured":"Jung N, Lee G. Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning. Adv Eng Inf. 2019;41:1\u201310.","journal-title":"Adv Eng Inf"},{"key":"1776_CR31","unstructured":"Javan N A, Jebreili A, Mozafari B, et al. Classification and segmentation of pulmonary lesions in CT images using a combined VGG-XGboost method, and an integrated Fuzzy Clustering-Level Set technique. 2021."},{"issue":"2","key":"1776_CR32","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.im.2017.05.007","volume":"55","author":"VG Remani","year":"2017","unstructured":"Remani VG, Brown JR, Shanker M, et al. An information supply chain system view for managing rare infectious diseases: the need to improve timeliness. Inf Manag. 2017;55(2):215\u201323.","journal-title":"Inf Manag"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01776-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-01776-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-01776-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T04:32:06Z","timestamp":1644985926000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-01776-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,16]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1776"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-01776-y","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,16]]},"assertion":[{"value":"21 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 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 approved by the Medical Science Research Ethics Committee of Peking University Third Hospital (Serial No. IRB00006761-M2020318). Informed consent from the patients was exempt due to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.","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":"None declared.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"41"}}