{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:53:23Z","timestamp":1742943203636,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030845285"},{"type":"electronic","value":"9783030845292"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-84529-2_24","type":"book-chapter","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T15:01:42Z","timestamp":1628521302000},"page":"284-293","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploration of Smart Medical Technology Based on Intelligent Computing Methods"],"prefix":"10.1007","author":[{"given":"Sijia","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yizhang","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"issue":"5","key":"24_CR1","doi-asserted-by":"publisher","first-page":"3029","DOI":"10.1007\/s10916-011-9780-4","volume":"36","author":"KB Wagholikar","year":"2012","unstructured":"Wagholikar, K.B., Sundararajan, V., Deshpande, A.W.: Modeling paradigms for medical diagnostic decision support: a survey and future directions. J. Med. Syst. 36(5), 3029\u20133049 (2012)","journal-title":"J. Med. Syst."},{"key":"24_CR2","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.ins.2020.06.025","volume":"538","author":"KN Qureshi","year":"2020","unstructured":"Qureshi, K.N., Din, S., Jeon, G., et al.: An accurate and dynamic predictive model for a smart M-health system using machine learning. Inf. Sci. 538, 486\u2013502 (2020)","journal-title":"Inf. Sci."},{"issue":"6","key":"24_CR3","doi-asserted-by":"publisher","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak, R.G., Lehnertz, K., Mormann, F., et al.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 64(6),  061907 (2001)","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Losing, V., Hammer, B., Wersing, H.: KNN classifier with self adjusting memory for heterogeneous concept drift. In: IEEE International Conference on Data Mining. IEEE (2017)","DOI":"10.1109\/ICDM.2016.0040"},{"issue":"6","key":"24_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.21037\/atm.2016.02.15","volume":"4","author":"Z Zhang","year":"2016","unstructured":"Zhang, Z.: Model building strategy for logistic regression: purposeful selection. Ann. Transl. Med. 4(6), 111 (2016)","journal-title":"Ann. Transl. Med."},{"key":"24_CR6","first-page":"175","volume":"14","author":"Shao, M.H., University, S.M.","year":"2018","unstructured":"Shao, M.H., University, S.M.: A review of typical decision tree algorithms. Comput. Knowl. Technol. 14, 175\u2013177 (2018)","journal-title":"Comput. Knowl. Technol."},{"issue":"3","key":"24_CR7","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/bju.14397","volume":"122","author":"J Ishioka","year":"2018","unstructured":"Ishioka, J., Matsuoka, Y., Uehara, S., et al.: Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm. BJU Int. 122(3), 411\u2013417 (2018)","journal-title":"BJU Int."},{"issue":"47","key":"24_CR8","doi-asserted-by":"publisher","first-page":"78140","DOI":"10.18632\/oncotarget.11293","volume":"7","author":"YD Zhang","year":"2016","unstructured":"Zhang, Y.D., Wang, J., Wu, C.J., et al.: An imaging-based approach predicts clinical outcomes in prostate cancer through a novel support vector machine classification. Oncotarget 7(47), 78140 (2016)","journal-title":"Oncotarget"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Surangsrirat, D., Thanawattano, C., Pongthornseri, R., et al.: Support vector machine classification of Parkinson\u2019s disease and essential tremor subjects based on temporal fluctuation. In: Engineering in Medicine and Biology Society, pp. 6389\u20136392. IEEE (2016)","DOI":"10.1109\/EMBC.2016.7592190"},{"key":"24_CR10","doi-asserted-by":"publisher","first-page":"127600","DOI":"10.1109\/ACCESS.2019.2937657","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Dong, J., Zhu, J., et al.: Common and special knowledge-driven TSK fuzzy system and its modeling and application for epileptic EEG signals recognition. IEEE Access 7, 127600\u2013127614 (2019)","journal-title":"IEEE Access"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-84529-2_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:03:28Z","timestamp":1710255808000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-84529-2_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030845285","9783030845292"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-84529-2_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2021a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2021\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}