{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:20:36Z","timestamp":1781112036460,"version":"3.54.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T00:00:00Z","timestamp":1599696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["7182137"],"award-info":[{"award-number":["7182137"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Innovation Fund of Peking Union Medical College","award":["2018-1002-01-10"],"award-info":[{"award-number":["2018-1002-01-10"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s13042-020-01192-6","type":"journal-article","created":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T18:03:52Z","timestamp":1599761032000},"page":"625-633","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Toward better prediction of recurrence for Cushing\u2019s disease: a factorization-machine based neural approach"],"prefix":"10.1007","volume":"12","author":[{"given":"Yanghua","family":"Fan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongfang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingcai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renzhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,10]]},"reference":[{"key":"1192_CR1","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1210\/jc.2012-3910","volume":"98","author":"D Abdelmannan","year":"2013","unstructured":"Abdelmannan D, Chaiban J, Selman WR, Arafah BM (2013) Recurrences of ACTH-secreting adenomas after pituitary adenomectomy can be accurately predicted by perioperative measurements of plasma ACTH levels. J Clin Endocrinol Metab 98:1458\u20131465. https:\/\/doi.org\/10.1210\/jc.2012-3910","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR2","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1530\/EJE-12-0921","volume":"168","author":"KI Alexandraki","year":"2013","unstructured":"Alexandraki KI et al (2013) Long-term remission and recurrence rates in Cushing\u2019s disease: predictive factors in a single-centre study. Eur J Endocrinol 168:639\u2013648. https:\/\/doi.org\/10.1530\/EJE-12-0921","journal-title":"Eur J Endocrinol"},{"key":"1192_CR3","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/s11102-014-0567-8","volume":"18","author":"G Aranda","year":"2015","unstructured":"Aranda G et al (2015) Long-term remission and recurrence rate in a cohort of Cushing\u2019s disease: the need for long-term follow-up. Pituitary 18:142\u2013149. https:\/\/doi.org\/10.1007\/s11102-014-0567-8","journal-title":"Pituitary"},{"key":"1192_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2944079","author":"A Artusi","year":"2019","unstructured":"Artusi A, Banterle F, Carrara F, Moreo A (2019) Efficient evaluation of image quality via deep-learning approximation of perceptual metrics. IEEE Trans Image Process. https:\/\/doi.org\/10.1109\/TIP.2019.2944079","journal-title":"IEEE Trans Image Process"},{"key":"1192_CR5","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1210\/jc.2016-3908","volume":"102","author":"D Asuzu","year":"2017","unstructured":"Asuzu D et al (2017) Normalized early postoperative cortisol and ACTH values predict nonremission after surgery for Cushing disease. J Clin Endocrinol Metab 102:2179\u20132187. https:\/\/doi.org\/10.1210\/jc.2016-3908","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR6","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2019.3512","author":"KD Bojikian","year":"2019","unstructured":"Bojikian KD, Lee CS, Lee AY (2019) Finding glaucoma in color fundus photographs using deep learning. JAMA Ophthalmol. https:\/\/doi.org\/10.1001\/jamaophthalmol.2019.3512","journal-title":"JAMA Ophthalmol"},{"key":"1192_CR7","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1227\/NEU.0000000000001011","volume":"78","author":"WF Chandler","year":"2016","unstructured":"Chandler WF, Barkan AL, Hollon T, Sakharova A, Sack J, Brahma B, Schteingart DE (2016) Outcome of transsphenoidal surgery for Cushing disease: a single-center experience over 32 years. Neurosurgery 78:216\u2013223. https:\/\/doi.org\/10.1227\/NEU.0000000000001011","journal-title":"Neurosurgery"},{"key":"1192_CR8","doi-asserted-by":"publisher","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","volume":"35","author":"A Cheerla","year":"2019","unstructured":"Cheerla A, Gevaert O (2019) Deep learning with multimodal representation for pancancer prognosis prediction. Bioinformatics 35:i446\u2013i454. https:\/\/doi.org\/10.1093\/bioinformatics\/btz342","journal-title":"Bioinformatics"},{"key":"1192_CR9","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz769","author":"R Chen","year":"2019","unstructured":"Chen R, Yang L, Goodison S, Sun Y (2019) Deep learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics. https:\/\/doi.org\/10.1093\/bioinformatics\/btz769","journal-title":"Bioinformatics"},{"key":"1192_CR10","doi-asserted-by":"crossref","unstructured":"Cheng H-T et al. (2016) Wide and deep learning for recommender systems. Paper presented at the Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA","DOI":"10.1145\/2988450.2988454"},{"key":"1192_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0583-3","author":"P Courtiol","year":"2019","unstructured":"Courtiol P et al (2019) Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat Med. https:\/\/doi.org\/10.1038\/s41591-019-0583-3","journal-title":"Nat Med"},{"key":"1192_CR12","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y, Li Z, He X (2017) DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:170304247","DOI":"10.24963\/ijcai.2017\/239"},{"key":"1192_CR13","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1038\/s41586-019-1559-7","volume":"573","author":"YG Ham","year":"2019","unstructured":"Ham YG, Kim JH, Luo JJ (2019) Deep learning for multi-year ENSO forecasts. Nature 573:568\u2013572. https:\/\/doi.org\/10.1038\/s41586-019-1559-7","journal-title":"Nature"},{"key":"1192_CR14","doi-asserted-by":"publisher","first-page":"9","DOI":"10.3171\/JNS\/2008\/108\/01\/0009","volume":"108","author":"BM Hofmann","year":"2008","unstructured":"Hofmann BM, Hlavac M, Martinez R, Buchfelder M, Muller OA, Fahlbusch R (2008) Long-term results after microsurgery for Cushing disease: experience with 426 primary operations over 35 years. J Neurosurg 108:9\u201318. https:\/\/doi.org\/10.3171\/JNS\/2008\/108\/01\/0009","journal-title":"J Neurosurg"},{"key":"1192_CR15","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1530\/EJE-17-0873","volume":"178","author":"N Ironside","year":"2018","unstructured":"Ironside N et al (2018) Earlier post-operative hypocortisolemia may predict durable remission from Cushing\u2019s disease. Eur J Endocrinol 178:255\u2013263. https:\/\/doi.org\/10.1530\/EJE-17-0873","journal-title":"Eur J Endocrinol"},{"key":"1192_CR16","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1210\/jc.2012-2893","volume":"98","author":"JK Lambert","year":"2013","unstructured":"Lambert JK, Goldberg L, Fayngold S, Kostadinov J, Post KD, Geer EB (2013) Predictors of mortality and long-term outcomes in treated Cushing\u2019s disease: a study of 346 patients. J Clin Endocrinol Metab 98:1022\u20131030. https:\/\/doi.org\/10.1210\/jc.2012-2893","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR17","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1210\/jc.2011-0456","volume":"96","author":"JR Lindsay","year":"2011","unstructured":"Lindsay JR, Oldfield EH, Stratakis CA, Nieman LK (2011) The postoperative basal cortisol and CRH tests for prediction of long-term remission from Cushing\u2019s disease after transsphenoidal surgery. J Clin Endocrinol Metab 96:2057\u20132064. https:\/\/doi.org\/10.1210\/jc.2011-0456","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR18","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1159\/000496753","volume":"108","author":"Y Liu","year":"2019","unstructured":"Liu Y et al (2019) Prediction of recurrence after transsphenoidal surgery for Cushing\u2019s disease: the use of machine learning algorithms. Neuroendocrinology 108:201\u2013210. https:\/\/doi.org\/10.1159\/000496753","journal-title":"Neuroendocrinology"},{"key":"1192_CR19","doi-asserted-by":"publisher","first-page":"2807","DOI":"10.1210\/jc.2015-1818","volume":"100","author":"LK Nieman","year":"2015","unstructured":"Nieman LK et al (2015) Treatment of Cushing\u2019s syndrome: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 100:2807\u20132831. https:\/\/doi.org\/10.1210\/jc.2015-1818","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR20","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1210\/jc.2008-0125","volume":"93","author":"LK Nieman","year":"2008","unstructured":"Nieman LK, Biller BM, Findling JW, Newell-Price J, Savage MO, Stewart PM, Montori VM (2008) The diagnosis of Cushing\u2019s syndrome: an endocrine society clinical practice guideline. J Clin Endocrinol Metab 93:1526\u20131540. https:\/\/doi.org\/10.1210\/jc.2008-0125","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/jcm8111951","author":"H Nishioka","year":"2019","unstructured":"Nishioka H, Yamada S (2019) Cushing's disease. J Clin Med. https:\/\/doi.org\/10.3390\/jcm8111951","journal-title":"J Clin Med"},{"key":"1192_CR22","doi-asserted-by":"publisher","first-page":"E13","DOI":"10.3171\/2014.10.FOCUS14682","volume":"38","author":"AV Pendharkar","year":"2015","unstructured":"Pendharkar AV, Sussman ES, Ho AL, Hayden Gephart MG, Katznelson L (2015) Cushing\u2019s disease: predicting long-term remission after surgical treatment. Neurosurg Focus 38:E13. https:\/\/doi.org\/10.3171\/2014.10.FOCUS14682","journal-title":"Neurosurg Focus"},{"key":"1192_CR23","doi-asserted-by":"publisher","unstructured":"Qu Y, Cai H, Ren K, Zhang W, Yu Y, Wen Y, Wang J (2016) Product-based neural networks for user response prediction. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), 12\u201315 Dec. 2016. pp 1149\u20131154. https:\/\/doi.org\/10.1109\/ICDM.2016.0151","DOI":"10.1109\/ICDM.2016.0151"},{"key":"1192_CR24","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1210\/jc.2018-02524","volume":"104","author":"O Ragnarsson","year":"2019","unstructured":"Ragnarsson O et al (2019) Overall and disease-specific mortality in patients with Cushing disease: a Swedish nationwide study. J Clin Endocrinol Metab 104:2375\u20132384. https:\/\/doi.org\/10.1210\/jc.2018-02524","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR25","doi-asserted-by":"crossref","unstructured":"Rendle S (2010) Factorization machines. In: 2010 IEEE International Conference on Data Mining. IEEE, pp 995\u20131000","DOI":"10.1109\/ICDM.2010.127"},{"key":"1192_CR26","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) \"Why should i trust you?\": Explaining the predictions of any classifier","DOI":"10.18653\/v1\/N16-3020"},{"key":"1192_CR27","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.wneu.2015.11.046","volume":"87","author":"M Shirvani","year":"2016","unstructured":"Shirvani M, Motiei-Langroudi R, Sadeghian H (2016) Outcome of microscopic transsphenoidal surgery in cushing disease: a case series of 96 patients. World Neurosurg 87:170\u2013175. https:\/\/doi.org\/10.1016\/j.wneu.2015.11.046","journal-title":"World Neurosurg"},{"key":"1192_CR28","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1210\/jcem.81.7.8675592","volume":"81","author":"N Sonino","year":"1996","unstructured":"Sonino N, Zielezny M, Fava GA, Fallo F, Boscaro M (1996) Risk factors and long-term outcome in pituitary-dependent Cushing\u2019s disease. J Clin Endocrinol Metab 81:2647\u20132652. https:\/\/doi.org\/10.1210\/jcem.81.7.8675592","journal-title":"J Clin Endocrinol Metab"},{"key":"1192_CR29","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1227\/NEU.0b013e31827b966a","volume":"72","author":"RM Starke","year":"2013","unstructured":"Starke RM, Reames DL, Chen CJ, Laws ER, Jane JA Jr (2013) Endoscopic transsphenoidal surgery for cushing disease: techniques, outcomes, and predictors of remission. Neurosurgery 72:240\u2013247. https:\/\/doi.org\/10.1227\/NEU.0b013e31827b966a(discussion 247)","journal-title":"Neurosurgery"},{"key":"1192_CR30","doi-asserted-by":"publisher","DOI":"10.1111\/joim.12975","author":"NA Tritos","year":"2019","unstructured":"Tritos NA, Biller BMK (2019) Current management of Cushing's disease. J Intern Med. https:\/\/doi.org\/10.1111\/joim.12975","journal-title":"J Intern Med"},{"key":"1192_CR31","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2019.2335","author":"HH Wang","year":"2019","unstructured":"Wang HH, Wang YH, Liang CW, Li YC (2019) Assessment of deep learning using nonimaging information and sequential medical records to develop a prediction model for nonmelanoma skin cancer. JAMA Dermatol. https:\/\/doi.org\/10.1001\/jamadermatol.2019.2335","journal-title":"JAMA Dermatol"},{"key":"1192_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2019.08.075","author":"H Xiong","year":"2019","unstructured":"Xiong H et al (2019) Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBio Med. https:\/\/doi.org\/10.1016\/j.ebiom.2019.08.075","journal-title":"EBio Med"},{"key":"1192_CR33","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1046\/j.0300-0664.2001.01444.x","volume":"56","author":"LB Yap","year":"2002","unstructured":"Yap LB, Turner HE, Adams CB, Wass JA (2002) Undetectable postoperative cortisol does not always predict long-term remission in Cushing\u2019s disease: a single centre audit. Clin Endocrinol (Oxf) 56:25\u201331. https:\/\/doi.org\/10.1046\/j.0300-0664.2001.01444.x","journal-title":"Clin Endocrinol (Oxf)"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01192-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-020-01192-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-020-01192-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T23:50:14Z","timestamp":1631231414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-020-01192-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,10]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["1192"],"URL":"https:\/\/doi.org\/10.1007\/s13042-020-01192-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,10]]},"assertion":[{"value":"27 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}