{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:45:51Z","timestamp":1787312751771,"version":"build-2736575974"},"reference-count":54,"publisher":"The Endocrine Society","issue":"1","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100011176","name":"Peking Union Medical College","doi-asserted-by":"publisher","award":["2018-1002-01-10"],"award-info":[{"award-number":["2018-1002-01-10"]}],"id":[{"id":"10.13039\/501100011176","id-type":"DOI","asserted-by":"publisher"}]},{"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":"Capital Characteristic Clinic Project","award":["Z16100000516092"],"award-info":[{"award-number":["Z16100000516092"]}]},{"DOI":"10.13039\/501100005150","name":"Chinese Academy of Medical Sciences","doi-asserted-by":"publisher","award":["2017-I2M-3-014"],"award-info":[{"award-number":["2017-I2M-3-014"]}],"id":[{"id":"10.13039\/501100005150","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Context<\/jats:title>\n                    <jats:p>Postoperative hypercortisolemia mandates further therapy in patients with Cushing\u2019s disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features and applied 5 ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model\u2013agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eighty-eight (43.8%) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had a low body mass index, a Knosp grade of III\u2013IV, and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grading and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC, and age were the most important features, and showed the reliability and clinical practicability of the Adaboost model in DC prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning\u2013based models could serve as an effective noninvasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1210\/clinem\/dgaa698","type":"journal-article","created":{"date-parts":[[2020,10,30]],"date-time":"2020-10-30T02:40:56Z","timestamp":1604025656000},"page":"e217-e231","source":"Crossref","is-referenced-by-count":34,"title":["Development of Machine Learning Models for Predicting Postoperative Delayed Remission in Patients With Cushing\u2019s Disease"],"prefix":"10.1210","volume":"106","author":[{"given":"Yanghua","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichao","family":"Li","sequence":"additional","affiliation":[{"name":"DHC Software Co. 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