{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:09:39Z","timestamp":1789711779875,"version":"4.0.1"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To summarize the current production and release trends of medical science popularization videos, analyze the effect of non-medical factors on their spread, and develop dissemination-prediction models using machine learning (ML) algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We identified a sample of medical science popularization videos on TikTok (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f566), Bilibili (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f50), Xiaohongshu (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f54), and International TikTok (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f46) platforms. Thirty six non-medical features were annotated as predictor variables, with \u201cThumb-Up,\u201d \u201cComment,\u201d \u201cShare\u201d and \u201cCollection\u201d as outcomes. Fifteen algorithms models were constructed for each outcome using the TikTok dataset and validated on the remaining three datasets, with model performance evaluated by area under the curve (AUC) and Brier score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the quantitative analysis of the 4 outcomes, we identified significant disparities among different videos, \u201cThumb-Up\u201d with a range from 0 to 2.72 million, \u201cCollection\u201d with 1 to 1.36 million, \u201cShare\u201d with 1 to 898 thousand, and \u201cComment\u201d with 0 and 200 thousand. Subsequently, four best-performing models were ultimately confirmed through internal and external validation, all of which were RF models, including \u201cThumb-Up\u201d (AUC\u202f=\u202f0.8802), \u201cCollection\u201d (AUC\u202f=\u202f0.7685), \u201cShare\u201d (AUC\u202f=\u202f0.7872), \u201cComment\u201d (AUC\u202f=\u202f0.8077). Weight analysis identified the video duration, video description length, and shooting in department office emerged as the most three crucial parameters across all four models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the significant impact of non-medical factors on the dissemination of medical science popularization videos, and shows that prediction models base on these factors can effectively forecast video spread and popularity. These findings may contribute to enhancing These findings, thereby advancing health education and strengthen public health literacy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1882986","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:38:41Z","timestamp":1789709921000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning prediction models for the popularization and dissemination of medical science popularization videos"],"prefix":"10.3389","volume":"14","author":[{"given":"Nuo","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Gynecology, the First Hospital of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiu-Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, the First Hospital of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Xue","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of General Practice, the First Hospital of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Smart Hospital Management Department, the First Hospital of China Medical University","place":["Shenyang, China"]},{"name":"Enduring Medicine Smart Innovation Research Institute","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Goizueta School of Business, Emory University","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Ning","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Cardiology, the First Hospital of China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da-Xin","family":"Gong","sequence":"additional","affiliation":[{"name":"Smart Hospital Management Department, the First Hospital of China Medical University","place":["Shenyang, China"]},{"name":"The Internet Hospital Branch of the Chinese Research Hospital Association","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghui","family":"Yuan","sequence":"additional","affiliation":[{"name":"Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Clinical Research Center for Malignant Tumor of Liaoning Province","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Zang","sequence":"additional","affiliation":[{"name":"Department of Community Nursing, School of Nursing, China Medical University","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang-Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of General Practice, the First Hospital of China Medical University","place":["Shenyang, China"]},{"name":"Smart Hospital Management Department, the First Hospital of China Medical University","place":["Shenyang, China"]},{"name":"Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Clinical Research Center for Malignant Tumor of Liaoning Province","place":["Shenyang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1186\/s12903-023-02872-9","article-title":"#OMFSurgery: analyzing the use of social media applications in oral and maxillofacial surgery resident training","volume":"23","author":"Seifert","year":"2023","journal-title":"BMC 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