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Comput. Healthcare"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the variables in longitudinal data. In this article, we propose a Trend Analysis Neural Networks (TANN), which models the evolution trend of the variables by adaptive feature learning. TANN was tested on dataset of Kaiuan research. The task was to predict occurrence of cardiovascular events within 2 and 5 years, with three repeated medical examinations during 2008 and 2013. For 2-year prediction, The AUC of the TANN is 0.7378, which is a significant improvement than that of conventional methods, while that of TRNS, RNN, DNN, GBDT, RF, and LR are 0.7222, 0.7034, 0.7054, 0.7136, 0.7160, and 0.7024, respectively. For 5-year prediction, TANN also shows improvement. The experimental results show that the proposed TANN achieves better prediction performance on cardiovascular events prediction than conventional models. Furthermore, by analyzing the weights of TANN, we could find out important trends of the indicators, which are ignored by conventional machine learning models. The trend discovery mechanism interprets the model well. TANN is an appropriate balance between high performance and interpretability.<\/jats:p>","DOI":"10.1145\/3648105","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T12:18:14Z","timestamp":1708345094000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Trend Analysis Neural Networks for Longitudinal Data Analysis"],"prefix":"10.1145","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1027-637X","authenticated-orcid":false,"given":"Zhenjie","family":"Yao","sequence":"first","affiliation":[{"name":"Institute of Microelectronics, Key Laboratory of Fabrication Technologies for Integrated Circuits, Chinese Academy of Sciences, Beijing, China and Purple Mountain Laboratory, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3704-4432","authenticated-orcid":false,"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Washington University, St. Louis, MO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0128-8408","authenticated-orcid":false,"given":"Jinwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and National Institute of Health Data Science at Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6110-7635","authenticated-orcid":false,"given":"Junjuan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-7471","authenticated-orcid":false,"given":"Shuohua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7095-6022","authenticated-orcid":false,"given":"Shouling","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Kailuan General Hospital Affiliated to North China University of Science and Technology, Tangshan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2923-958X","authenticated-orcid":false,"given":"Yanhui","family":"Tu","sequence":"additional","affiliation":[{"name":"Shandong Future Network Research Institute, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3340-3108","authenticated-orcid":false,"given":"Ming-Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Institute of Health Data Science at Peking University, Beijing, China and Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and Chinese Academy of Medical Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2349-2936","authenticated-orcid":false,"given":"Luxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Institute of Health Data Science at Peking University, Beijing, China and Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China and Chinese Academy of Medical Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"issue":"8","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1097\/HJH.0000000000000230","article-title":"Resting heart rate and risk of hypertension: Results of the Kailuan cohort study","volume":"32","author":"Anxin Wang","year":"2014","unstructured":"Wang Anxin, Liu Xiaoxue, Guo Xiuhua, Dong Yan, Wu Yuntao, Huang Zhe, Xing Aijun, Luo Yanxia, Jost B. 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