{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:44:47Z","timestamp":1790786687708,"version":"4.1.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Japan Sciences Research Grant for Research on Intractable Diseases"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration\/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.<\/jats:p>","DOI":"10.1038\/s41598-021-96019-x","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:03:40Z","timestamp":1628748220000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis"],"prefix":"10.1038","volume":"11","author":[{"given":"Jun","family":"Miyoshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsubasa","family":"Maeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katsuyoshi","family":"Matsuoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daisuke","family":"Saito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sawako","family":"Miyoshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minoru","family":"Matsuura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susumu","family":"Okamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Tamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadakazu","family":"Hisamatsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"96019_CR1","doi-asserted-by":"publisher","first-page":"1576","DOI":"10.1053\/j.gastro.2003.09.036","volume":"125","author":"KV Winther","year":"2003","unstructured":"Winther, K. 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Bowel Dis."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-96019-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-96019-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-96019-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T07:24:52Z","timestamp":1670052292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-96019-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,12]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["96019"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-96019-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,12]]},"assertion":[{"value":"8 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Jun Miyoshi has received lecture fees from JIMRO Co. and Takeda Pharmaceutical Co. Ltd. Katsuyoshi Matsuoka has served as a scientific adviser for EA Pharma; served on advisory boards for Bristol-Meyers Squibb, and Eli Lilly and Company; received speaker fees from Mitsubishi Tanabe Pharma, Takeda Pharmaceutical, Janssen, AbbVie, EA Pharma, Pfizer Inc, Mochida Pharmaceutical, Kyorin Pharmaceutical, Zeria Pharmaceutical, Kissei Pharmacetucal, and JIMRO; and received research grants from Mitsubishi Tanabe Pharma, Mochida Pharmaceutical, Kyorin Pharmaceutical, AbbVie, Takeda Pharmaceutical, Nippon Kayaku, EA Pharma, Kissei Pharmaceutical, and JIMRO Co. Minoru Matsuura has received consulting and lecture fees from Janssen Pharmaceutical K.K., Takeda Pharmaceutical Co. Ltd., AbbVie GK, Mitsubishi Tanabe Pharma Corporation, Kyorin Pharmaceutical Co. Ltd., Mochida Pharmaceutical Co., Ltd., JIMRO Co., Nippon Kayaku Co. Ltd., Mylan EPD G.K., and Aspen Japan Co. Ltd. Tadakazu Hisamatsu has performed Joint Research with Alfresa Pharma Co. Ltd., EA Pharma Co. Ltd.; received grant support from Mitsubishi Tanabe Pharma Corporation, EA Pharma Co. Ltd., AbbVie GK, JIMRO Co. Ltd., Zeria Pharmaceutical Co. Ltd., Daiichi-Sankyo, Kyorin Pharmaceutical Co. Ltd., Nippon Kayaku Co. Ltd., Takeda Pharmaceutical Co. Ltd., Pfizer Inc., Mochida Pharmaceutical Co., Ltd.; and received consulting and lecture fees from EA Pharma Co. Ltd., AbbVie GK, Celgene K.K., Janssen Pharmaceutical K.K., Pfizer Inc., Nichi-Iko Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corporation , Kyorin Pharmaceutical Co. Ltd., JIMRO Co., Mochida Pharmaceutical Co., Ltd., and Takeda Pharmaceutical Co. Ltd. Tsubasa Maeda, Daisuke Saito, Sawako Miyoshi, Susumu Okamoto, and Satoshi Tamura have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16440"}}