乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T23:49:50Z","timestamp":1726184990024},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811992964"},{"type":"electronic","value":"9789811992971"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-9297-1_24","type":"book-chapter","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T10:33:02Z","timestamp":1674124382000},"page":"333-349","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Postoperative Survival Level of Esophageal Cancer Patients Based on Kaplan-Meier (K-M) Survival Analysis and Gray Wolf Optimization (GsWO)-BP Model"],"prefix":"10.1007","author":[{"given":"Enhao","family":"Liang","sequence":"first","affiliation":[]},{"given":"Yanfeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Lidong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xueke","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Changkai","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"issue":"6","key":"24_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6), 394\u2013424 (2018)","journal-title":"CA Cancer J Clin"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Cancer statistics in China, 2015.CA cancer J. Clin. 66(2), 115\u2013132 (2016)","DOI":"10.3322\/caac.21338"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015.CA cancer. J. Clin. 65 (1), 5\u201329 (2015)","DOI":"10.3322\/caac.21254"},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Chen, W., et al.: Cancer statistics in China, 2015.CA cancer. J. Clin. 66(2), 115\u201332 (2016)","DOI":"10.3322\/caac.21338"},{"issue":"11","key":"24_CR5","doi-asserted-by":"publisher","first-page":"2571","DOI":"10.1002\/cncr.24989","volume":"116","author":"M Yang","year":"2010","unstructured":"Yang, M., Zhang, H., Ma, Z., et al.: Log odds of positive lymph nodes is a novel prognostic indicator superior to the number based and the ratio-based N category for gastric cancer patients with R0resection. Cancer 116(11), 2571\u20132580 (2010)","journal-title":"Cancer"},{"issue":"2","key":"24_CR6","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.athoracsur.2016.03.030","volume":"102","author":"J Cao","year":"2016","unstructured":"Cao, J., Yuan, P., Ma, H., et al.: Log odds of positive lymph nodes predicts survival in patients after resection for esophageal cancer. Ann. Thorac. Surg. 102(2), 424\u2013432 (2016)","journal-title":"Ann. Thorac. Surg."},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Hou, H.F., et al.: Survival of esophageal cancer in China. A pooled analysis on hospital-based studies from 2000 to 2018. Front. Oncol. 9, 548 (2019)","DOI":"10.3389\/fonc.2019.00548"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D.A.: Jemal, \u201ccancer statistics,\u201d a cancer. J. Clin. 66(1), 7\u201330 (2017)","DOI":"10.3322\/caac.21332"},{"issue":"1","key":"24_CR9","first-page":"318","volume":"13","author":"S Senthil","year":"2018","unstructured":"Senthil, S., Ayshwarya, B.: Lung cancer prediction using feed forward back propagation neural networks with optimal features. Int. J. Appl. Eng. Res. 13(1), 318\u2013325 (2018)","journal-title":"Int. J. Appl. Eng. Res."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Pan, X.Y., et al.: Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models. Br. J. Radiol. 93(1112), 20190825 (2020)","DOI":"10.1259\/bjr.20190825"},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"181127","DOI":"10.1109\/ACCESS.2020.3028147","volume":"8","author":"Y Wang","year":"2020","unstructured":"Wang, Y., et al.: Prediction of survival time of patients with esophageal squamous cell carcinoma based on univariate analysis and ASSA-BP neural network. IEEE Access 8, 181127\u2013181136 (2020)","journal-title":"IEEE Access"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Song, W., et al.: A GPU-based training of BP Neural Network for Healthcare Data Analysis. Advanced Multimedia and Ubiquitous Engineering. Springer, Singapore, 518, 193\u2013198 (2018)","DOI":"10.1007\/978-981-13-1328-8_24"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Wu, X.Z., et al.: A study on the GA-BP neural network model for surface roughness of basswood-veneered medium-density fiberboard. Holzforschung 74(10), 979\u2013988 (2020)","DOI":"10.1515\/hf-2019-0248"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z.M., Li, Y.N.: A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS. BMC Med. Inform. Decis. Making 20(1), 1425\u20131431 (2020)","DOI":"10.1186\/s12911-020-01157-3"},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Mohamed, E.A.B.S., et al.: Novel hybridized adaptive neuro\u2010fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor. Fatig. Fract. Eng. Mater. Struct. 43(11), 2653\u20132667 (2020)","DOI":"10.1111\/ffe.13325"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Guo, W.J., et al.: Using a genetic algorithm to improve oil spill prediction. Marine Pollut. Bull. 135, 386\u2013396 (2018)","DOI":"10.1016\/j.marpolbul.2018.07.026"},{"issue":"44","key":"24_CR17","first-page":"413","volume":"44","author":"S Yoko","year":"2020","unstructured":"Yoko, S., et al.: Diagnostic performance of the support vector machine model for breast cancer on ring-shaped dedicated breast positron emission tomography images. J. Comput. Assist. Tomogr. 44(44), 413\u2013418 (2020)","journal-title":"J. Comput. Assist. Tomogr."},{"issue":"3","key":"24_CR18","first-page":"244","volume":"195","author":"N Imano","year":"2021","unstructured":"Imano, N., et al.: Evaluating individual radiosensitivity for the prediction of acute toxicities of chemoradiotherapy in esophageal cancer patients. Radiat. Res. 195(3), 244\u2013252 (2021)","journal-title":"Radiat. Res."},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Beukinga, R.J., et al.: Addition of HER2 and CD44 to 18F-FDG PET-based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer. Eur. Radiol. 2021(5) (2021)","DOI":"10.1007\/s00330-020-07439-8"},{"key":"24_CR20","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ejca.2021.07.012","volume":"155","author":"A Sk","year":"2021","unstructured":"Sk, A., et al.: Gastrointestinal cancer classification and prognostication from histology using deep learning systematic review. Eur. J. Cancer 155, 200\u2013215 (2021)","journal-title":"Eur. J. Cancer"},{"issue":"10","key":"24_CR21","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.14309\/ajg.0000000000001402","volume":"116","author":"D Carlson","year":"2021","unstructured":"Carlson, D., et al.: Prediction of esophageal retention, a study comparing high-resolution manometry and functional luminal imaging probe panometry. Am. J. Gastroenterol. 116(10), 2032\u20132041 (2021)","journal-title":"Am. J. Gastroenterol."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Southey, M.C., Dugue, P.A.: Improving breast cancer risk prediction with epigenetic risk factors. Nat. Rev. Clin. Oncol. 2022(19\u20136) (2022)","DOI":"10.1038\/s41571-022-00622-4"},{"issue":"9","key":"24_CR23","doi-asserted-by":"publisher","first-page":"5930","DOI":"10.1007\/s00330-022-08776-6","volume":"32","author":"F Chu","year":"2022","unstructured":"Chu, F., et al.: Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma. Eur. Radiol. 32(9), 5930\u20135942 (2022)","journal-title":"Eur. Radiol."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, H., et al.: Nomogram-Integrated C-Reactive protein\/albumin ratio predicts efficacy and prognosis in patients with thoracic esophageal squamous cell carcinoma receiving chemoradiotherapy. Cancer Manag. Res. 11, 9459\u20139468 (2019)","DOI":"10.2147\/CMAR.S228113"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Xia, S.J., et al.: Overexpression of PSMA7 predicts poor prognosis in patients with gastric cancer. Oncol. Lett. 18(5), 5341\u20135349 (2019)","DOI":"10.3892\/ol.2019.10879"}],"container-title":["Communications in Computer and Information Science","Data Mining and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-9297-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T11:35:18Z","timestamp":1674128118000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-9297-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811992964","9789811992971"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-9297-1_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DMBD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Mining and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dmbd2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"135","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2-3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}