{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:07:02Z","timestamp":1770833222911,"version":"3.50.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:00:00Z","timestamp":1770768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      The purpose of this study is to evaluate the predictive accuracy of a machine learning-based herbal medicine response prediction algorithm in patients with functional dyspepsia (FD). In a preliminary clinical study, the algorithm was developed using the XGBoost regressor framework to predict the relative effect sizes of three commonly prescribed herbal formulations\u2014\n                      <jats:italic>Yijung-tang<\/jats:italic>\n                      (\n                      <jats:italic>Lizhong-tang<\/jats:italic>\n                      ),\n                      <jats:italic>Pyeongwi-san<\/jats:italic>\n                      (\n                      <jats:italic>Pingwei-san<\/jats:italic>\n                      ), and\n                      <jats:italic>Shihosogan-tang<\/jats:italic>\n                      (\n                      <jats:italic>Chaihu Shugan-tang<\/jats:italic>\n                      ). The prediction system recommends the formulation expected to yield the greatest therapeutic benefit for each individual.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study is a randomized, assessor-blinded, parallel-group, open-label, multicenter clinical trial. A total of 100 patients with FD will be recruited from two Korean medical hospitals and randomly assigned to either the ACCORD group (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f50), which will receive treatment guided by the machine learning algorithm, or the DISCORD group (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f50), which will receive one of the two treatments not recommended by the algorithm. Patients will take the assigned herbal medicine for 8\u202fweeks, three times daily, between meals.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcomes<\/jats:title>\n                    <jats:p>The primary outcome will be gastrointestinal symptom score. Secondary outcomes will include total dyspepsia symptom score, adequate relief of dyspepsia, overall treatment effect, visual analog scale score, functional dyspepsia\u2013related quality of life, and pattern identification questionnaire results. Exploratory outcomes will include blood and fecal metabolome analysis, fecal and salivary microbiota profiling, and measurements obtained using Korean medicine diagnostic devices (heart rate variability, tongue, pulse, and abdominal diagnosis).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Integrating a machine learning-based prediction system into treatment strategies for FD may enhance clinical practice and support the broader adoption of artificial intelligence-driven approaches in personalized medicine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>\n                      Clinical Research information Service (registration number: KCT0010587) and Open Science Framework (\n                      <jats:ext-link>https:\/\/osf.io\/2ecz8<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1716891","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T17:00:04Z","timestamp":1770829204000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction of herbal medicine response in functional dyspepsia: protocol for a randomized, assessor-blinded, multicenter trial"],"prefix":"10.3389","volume":"13","author":[{"given":"Chaehyun","family":"Park","sequence":"first","affiliation":[{"name":"Department of Korean Internal Medicine, College of Korean Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong","place":["Seoul, Republic of 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Medicine","place":["Daejeon, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi Young","family":"Lim","sequence":"additional","affiliation":[{"name":"Personalized Diet Research Group, Food Functionality Research Division, Korea Food Research Institute","place":["Wanju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghyun","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Diagnostics and Biofunctional Medicine, College of Korean Medicine, Sangji University","place":["Wonju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong-Jun","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Internal Korean Medicine, Dongguk University Ilsan Oriental Medical Hospital","place":["Goyang, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun-Hwan","family":"Lee","sequence":"additional","affiliation":[{"name":"KM Science Research Division, Korea Institute of Oriental Medicine","place":["Daejeon, Republic of Korea"]},{"name":"Korean Medicine Life Science, University of Science and Technology, Campus of Korea Institute of Oriental Medicine","place":["Daejeon, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jae-Woo","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Korean Internal Medicine, College of Korean Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong","place":["Seoul, Republic of Korea"]},{"name":"Department of Digestive Diseases, College of Korean Medicine, Kyung Hee University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seok-Jae","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Korean Internal Medicine, College of Korean Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong","place":["Seoul, Republic of Korea"]},{"name":"Department of Digestive Diseases, College of Korean Medicine, Kyung Hee University","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hojun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine of Korean Medicine, College of Korean Medicine, Dongguk University","place":["Gyeongju, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1016\/S0140-6736(20)30469-4","article-title":"Functional 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