{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T04:59:54Z","timestamp":1762491594539,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032093202","type":"print"},{"value":"9783032093219","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:00:00Z","timestamp":1762560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-09321-9_15","type":"book-chapter","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T04:58:02Z","timestamp":1762491482000},"page":"216-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Imputation for\u00a0Noisy Time-Series Data in\u00a0Healthcare"],"prefix":"10.1007","author":[{"given":"Lien P.","family":"Le","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi Xuan-Hien","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thu","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael A.","family":"Riegler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P\u00e5l","family":"Halvorsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binh T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Belani, S., Wahood, W., Hardigan, P., Placzek, A.N., Ely, S.: Accuracy of detecting atrial fibrillation: a systematic review and meta-analysis of wrist-worn wearable technology. Cureus 13(12) (2021)","DOI":"10.7759\/cureus.20362"},{"key":"15_CR2","unstructured":"Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: BRITS: bidirectional recurrent imputation for time series. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"1","key":"15_CR3","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)","journal-title":"Sci. Rep."},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Du, W., Cote, D., Liu, Y.: SAITS: Self-attention-based imputation for time series. Expert Syst. Appl. 219, 119619 (2023). https:\/\/doi.org\/10.1016\/j.eswa.2023.119619. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417423001203","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Garcia-Ceja, E., et al.: Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients. In: Proceedings of the 9th ACM on Multimedia Systems Conference, MMSys\u201918. ACM, New York (2018). https:\/\/doi.org\/10.1145\/3204949.3208125","DOI":"10.1145\/3204949.3208125"},{"issue":"1","key":"15_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s41597-025-04384-3","volume":"12","author":"E Garcia-Ceja","year":"2025","unstructured":"Garcia-Ceja, E., et al.: OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD. Sci. Data 12(1), 32 (2025)","journal-title":"Sci. Data"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja, E., et al.: HTAD: a home-tasks activities dataset with wrist-accelerometer and audio features. In: MultiMedia Modeling, pp. 196\u2013205. Springer International Publishing, Cham (2021)","DOI":"10.1007\/978-3-030-67835-7_17"},{"issue":"1","key":"15_CR8","doi-asserted-by":"publisher","first-page":"21594","DOI":"10.1038\/s41598-020-78447-3","volume":"10","author":"J Grauer","year":"2020","unstructured":"Grauer, J., L\u00f6wen, H., Liebchen, B.: Strategic spatiotemporal vaccine distribution increases the survival rate in an infectious disease like COVID-19. Sci. Rep. 10(1), 21594 (2020)","journal-title":"Sci. Rep."},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2019.06.007","volume":"360","author":"Z Guo","year":"2019","unstructured":"Guo, Z., Wan, Y., Ye, H.: A data imputation method for multivariate time series based on generative adversarial network. Neurocomputing 360, 185\u2013197 (2019)","journal-title":"Neurocomputing"},{"issue":"9","key":"15_CR10","doi-asserted-by":"publisher","first-page":"e0306303","DOI":"10.1371\/journal.pone.0306303","volume":"19","author":"V Hua","year":"2024","unstructured":"Hua, V., Nguyen, T., Dao, M.S., Nguyen, H.D., Nguyen, B.T.: The impact of data imputation on air quality prediction problem. PLoS ONE 19(9), e0306303 (2024)","journal-title":"PLoS ONE"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Jakobsen, P., et al.: PSYKOSE: a motor activity database of patients with schizophrenia. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 303\u2013308 (2020). https:\/\/doi.org\/10.1109\/CBMS49503.2020.00064","DOI":"10.1109\/CBMS49503.2020.00064"},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"100720","DOI":"10.1016\/j.cosrev.2024.100720","volume":"56","author":"LP Le","year":"2025","unstructured":"Le, L.P., Nguyen, T., Riegler, M.A., Halvorsen, P., Nguyen, B.T.: Multimodal missing data in healthcare: a comprehensive review and future directions. Comput. Sci. Rev. 56, 100720 (2025)","journal-title":"Comput. Sci. Rev."},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Le\u00a0Lien, P., Do, T.T., Nguyen, T.: Data imputation for multivariate time-series data. In: 2023 15th International Conference on Knowledge and Systems Engineering (KSE), pp.\u00a01\u20136. IEEE (2023)","DOI":"10.1109\/KSE59128.2023.10299484"},{"issue":"1","key":"15_CR14","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/s41597-022-01545-6","volume":"9","author":"H Lee","year":"2022","unstructured":"Lee, H., et al.: A large collection of real-world pediatric sleep studies. Sci. Data 9(1), 421 (2022)","journal-title":"Sci. Data"},{"issue":"19","key":"15_CR15","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1161\/CIRCULATIONAHA.122.060291","volume":"146","author":"SA Lubitz","year":"2022","unstructured":"Lubitz, S.A., et al.: Detection of atrial fibrillation in a large population using wearable devices: the Fitbit heart study. Circulation 146(19), 1415\u20131424 (2022)","journal-title":"Circulation"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"e17450179315688","DOI":"10.2174\/0117450179315688240607052117","volume":"20","author":"U Madububambachu","year":"2024","unstructured":"Madububambachu, U., Ukpebor, A., Ihezue, U.: Machine learning techniques to predict mental health diagnoses: a systematic literature review. Clin. Pract. Epidemiol. Mental Health: CP & EMH 20, e17450179315688 (2024)","journal-title":"Clin. Pract. Epidemiol. Mental Health: CP & EMH"},{"issue":"5","key":"15_CR17","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.2337\/dc19-1459","volume":"43","author":"MI Maiorino","year":"2020","unstructured":"Maiorino, M.I., et al.: Effects of continuous glucose monitoring on metrics of glycemic control in diabetes: a systematic review with meta-analysis of randomized controlled trials. Diab. Care 43(5), 1146\u20131156 (2020)","journal-title":"Diab. Care"},{"key":"15_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-023-00232-9","author":"ST Pingi","year":"2023","unstructured":"Pingi, S.T., Zhang, D., Bashar, M.A., Nayak, R.: Joint representation learning with generative adversarial imputation network for improved classification of longitudinal data. Data Sci. Eng. (2023). https:\/\/doi.org\/10.1007\/s41019-023-00232-9","journal-title":"Data Sci. Eng."},{"issue":"5","key":"15_CR19","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","volume":"22","author":"B Shickel","year":"2017","unstructured":"Shickel, B., Tighe, P.J., Bihorac, A., Rashidi, P.: Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J. Biomed. Health Inform. 22(5), 1589\u20131604 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"7","key":"15_CR20","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1177\/10547738231183207","volume":"32","author":"A Tran","year":"2023","unstructured":"Tran, A., Topp, R., Tarshizi, E., Shao, A.: Predicting the onset of sepsis using vital signs data: a machine learning approach. Clin. Nurs. Res. 32(7), 1000\u20131009 (2023)","journal-title":"Clin. Nurs. Res."},{"key":"15_CR21","doi-asserted-by":"publisher","unstructured":"van Buuren, S., Groothuis-Oudshoorn, K.: MICE: multivariate imputation by chained equations in R. J. Stat. Softw. 45(3), 1\u201367 (2011). https:\/\/doi.org\/10.18637\/jss.v045.i03","DOI":"10.18637\/jss.v045.i03"},{"key":"15_CR22","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1109\/LSP.2022.3224880","volume":"29","author":"AY Y\u0131ld\u0131z","year":"2022","unstructured":"Y\u0131ld\u0131z, A.Y., Ko\u00e7, E., Ko\u00e7, A.: Multivariate time series imputation with transformers. IEEE Signal Process. Lett. 29, 2517\u20132521 (2022)","journal-title":"IEEE Signal Process. Lett."},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Yin, C., Liu, R., Zhang, D., Zhang, P.: Identifying sepsis subphenotypes via time-aware multi-modal auto-encoder. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 862\u2013872 (2020)","DOI":"10.1145\/3394486.3403129"},{"key":"15_CR24","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ins.2020.11.035","volume":"551","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Zhou, B., Cai, X., Guo, W., Ding, X., Yuan, X.: Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Inf. Sci. 551, 67\u201382 (2021)","journal-title":"Inf. Sci."},{"key":"15_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-018-0616-8","volume":"18","author":"L Zhou","year":"2018","unstructured":"Zhou, L., Zhao, P., Wu, D., Cheng, C., Huang, H.: Time series model for forecasting the number of new admission inpatients. BMC Med. Inform. Decis. Mak. 18, 1\u201311 (2018)","journal-title":"BMC Med. Inform. Decis. Mak."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09321-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T04:58:04Z","timestamp":1762491484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09321-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,8]]},"ISBN":["9783032093202","9783032093219"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09321-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,8]]},"assertion":[{"value":"8 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}