{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:59:30Z","timestamp":1757455170224,"version":"3.41.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020YFB2104100"],"award-info":[{"award-number":["2020YFB2104100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71771131"],"award-info":[{"award-number":["71771131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model\u2019s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.<\/jats:p>","DOI":"10.1145\/3471571","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T01:18:12Z","timestamp":1634606292000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Multi-attention Collaborative Deep Learning Approach for Blood Pressure Prediction"],"prefix":"10.1145","volume":"13","author":[{"given":"Luo","family":"He","sequence":"first","affiliation":[{"name":"Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering, Schoolof Economics and Management, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Yang","sequence":"additional","affiliation":[{"name":"Graduate School of Management, University of California, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISDA.2009.230"},{"key":"e_1_3_1_3_2","unstructured":"Dzmitry Bahdanau Kyunghyun Cho and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Retrieved from http:\/\/arxiv.org\/abs\/1409.0473."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2014.0553"},{"key":"e_1_3_1_5_2","volume-title":"Classification and Regression Trees","author":"Breiman Leo","year":"1984","unstructured":"Leo Breiman, Jerome Friedman, Charles J. Stone, and Richard A. Olshen. 1984. Classification and Regression Trees. CRC Press."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001411009093"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67964-8_19"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy017"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz141"},{"key":"e_1_3_1_10_2","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Retrieved from https:\/\/arXiv:1406.1078.","journal-title":"Retrieved from https:\/\/arXiv:1406.1078"},{"key":"e_1_3_1_11_2","article-title":"Keras","author":"Chollet Fran\u00e7ois","year":"2015","unstructured":"Fran\u00e7ois Chollet et\u00a0al. 2015. Keras. Retrieved from https:\/\/keras.io.","journal-title":"Retrieved from https:\/\/keras.io"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocz101"},{"key":"e_1_3_1_13_2","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. Retrieved from https:\/\/arXiv:1412.3555.","journal-title":"Retrieved from https:\/\/arXiv:1412.3555"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw180"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2002.804943"},{"issue":"1","key":"e_1_3_1_16_2","first-page":"33","article-title":"The corbin and strauss chronic illness trajectory model: An update","volume":"12","author":"Corbin Juliet M.","year":"1998","unstructured":"Juliet M. Corbin. 1998. The corbin and strauss chronic illness trajectory model: An update. Res. Theory Nurs. Pract. 12, 1 (1998), 33.","journal-title":"Res. Theory Nurs. Pract."},{"issue":"3","key":"e_1_3_1_17_2","first-page":"155","article-title":"A nursing model for chronic illness management based upon the trajectory framework","volume":"5","author":"Corbin Juliet M.","year":"1991","unstructured":"Juliet M. Corbin and Anselm Strauss. 1991. A nursing model for chronic illness management based upon the trajectory framework. Scholar. Inquiry Nurs. Pract. 5, 3 (1991), 155\u2013174.","journal-title":"Scholar. Inquiry Nurs. Pract."},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/637635"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.137"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.5555\/2834535"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2274281"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.4103\/2470-7511.248374"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7471783"},{"key":"e_1_3_1_26_2","article-title":"Adam: A method for stochastic optimization","author":"Kingma Diederik P.","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. Retrieved from https:\/\/arXiv:1412.6980.","journal-title":"Retrieved from https:\/\/arXiv:1412.6980"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2011-720"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052604"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2017\/41.2.07"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113248"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2691715"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_3_1_33_2","first-page":"1","article-title":"Random forests","author":"Pavlov Yu L.","year":"2001","unstructured":"Yu L. Pavlov. 2001. Random forests. Random Forests (2001), 1\u2013122. https:\/\/doi.org\/10.1201\/9780429469275-8","journal-title":"Random Forests"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2017.04.001"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2017.05.012"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/SPACES.2018.8316330"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3107411.3107445"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP.2016.7501681"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/BHI.2018.8333434"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786984.2786995"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2019.113171"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2018.10.005"},{"key":"e_1_3_1_44_2","volume-title":"Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks","author":"Organization World Health","year":"2009","unstructured":"World Health Organization. 2009. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. World Health Organization."},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIAFS.2014.7069529"},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy068"},{"key":"e_1_3_1_47_2","volume-title":"Proceedings of the 39th International Conference on Information Systems (ICIS\u201918)","author":"Xie Jiaheng","year":"2018","unstructured":"Jiaheng Xie and Bin Zhang. 2018. Readmission risk prediction for patients with heterogeneous hazard: A trajectory-aware deep learning Approach. In Proceedings of the 39th International Conference on Information Systems (ICIS\u201918). Association for Information Systems."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2018.06.010"}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3471571","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3471571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:27Z","timestamp":1750191447000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3471571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,6,30]]}},"alternative-id":["10.1145\/3471571"],"URL":"https:\/\/doi.org\/10.1145\/3471571","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2021,10,18]]},"assertion":[{"value":"2021-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-18","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}