{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:39:11Z","timestamp":1765888751659,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Center for Machine Learning and Health at Carnegie Mellon University through the Pittsburgh Health Data Alliance","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hospital readmissions impose an extreme burden on both health systems and patients. Timely management of the postoperative complications that result in readmissions is necessary to mitigate the effects of these events. However, accurately predicting readmissions is very challenging, and current approaches demonstrated a limited ability to forecast which patients are likely to be readmitted. Our research addresses the challenge of daily readmission risk prediction after the hospital discharge via leveraging the abilities of mobile data streams collected from patients devices in a probabilistic deep learning framework. Through extensive experiments on a real-world dataset that includes smartphone and Fitbit device data from 49 patients collected for 60 days after discharge, we demonstrate our framework\u2019s ability to closely simulate the readmission risk trajectories for cancer patients.<\/jats:p>","DOI":"10.3390\/s21227510","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"7510","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of Hospital Readmission from Longitudinal Mobile Data Streams"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4028-4752","authenticated-orcid":false,"given":"Chen","family":"Qian","sequence":"first","affiliation":[{"name":"Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patraporn","family":"Leelaprachakul","sequence":"additional","affiliation":[{"name":"Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Landers","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carissa","family":"Low","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anind K.","family":"Dey","sequence":"additional","affiliation":[{"name":"Information School, University of Washington, Seattle, WA 98105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afsaneh","family":"Doryab","sequence":"additional","affiliation":[{"name":"Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA"},{"name":"Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1200\/JCO.2014.55.5938","article-title":"Exploring the burden of inpatient readmissions after major cancer surgery","volume":"33","author":"Stitzenberg","year":"2015","journal-title":"J. Clin. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e335","DOI":"10.1200\/JOP.17.00067","article-title":"Readmissions after complex cancer surgery: Analysis of the nationwide readmissions database","volume":"14","author":"Zafar","year":"2018","journal-title":"J. Oncol. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"m958","DOI":"10.1136\/bmj.m958","article-title":"Use of electronic medical records in development and validation of risk prediction models of hospital readmission: Systematic review","volume":"369","author":"Mahmoudi","year":"2020","journal-title":"BMJ"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s10620-019-05826-w","article-title":"Predicting 30-day Hospital readmission risk in a national cohort of patients with cirrhosis","volume":"65","author":"Koola","year":"2020","journal-title":"Dig. Dis. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.7309\/jmtm.5.2.2","article-title":"Mobile and wearable device features that matter in promoting physical activity","volume":"5","author":"Wang","year":"2016","journal-title":"J. Mob. Technol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1080\/14461242.2016.1211486","article-title":"Mobile, wearable and ingestible health technologies: Towards a critical research agenda","volume":"26","author":"Rich","year":"2017","journal-title":"Health Sociol. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Leijdekkers, P., and Gay, V. (2008, January 17\u201319). A self-test to detect a heart attack using a mobile phone and wearable sensors. Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems, Jyvaskyla, Finland.","DOI":"10.1109\/CBMS.2008.59"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3314395","article-title":"Modeling Biobehavioral Rhythms with Passive Sensing in the Wild: A Case Study to Predict Readmission Risk after Pancreatic Surgery","volume":"3","author":"Doryab","year":"2019","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bae, S., Dey, A.K., and Low, C.A. (2016, January 12\u201316). Using passively collected sedentary behavior to predict hospital readmission. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971750"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1093\/abm\/kax022","article-title":", III; Zureikat, A.H.; et al. Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission","volume":"52","author":"Low","year":"2018","journal-title":"Ann. Behav. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1001\/jamasurg.2014.2346","article-title":"Tracking early readmission after pancreatectomy to index and nonindex institutions: A more accurate assessment of readmission","volume":"150","author":"Tosoian","year":"2015","journal-title":"JAMA Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1278","DOI":"10.1378\/chest.120.4.1278","article-title":"Hospital readmission among long-term ventilator patients","volume":"120","author":"Douglas","year":"2001","journal-title":"Chest"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1007\/s11605-010-1326-4","article-title":"Redefining mortality after pancreatic cancer resection","volume":"14","author":"Carroll","year":"2010","journal-title":"J. Gastrointest. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104136","DOI":"10.1016\/j.ijmedinf.2020.104136","article-title":"Predicting Hospital Readmission in Patients with Mental or Substance Use Disorders: A Machine Learning Approach","volume":"139","author":"Morel","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1016\/j.jtcvs.2020.04.172","article-title":"Using machine learning to predict early readmission following esophagectomy","volume":"161","author":"Bolourani","year":"2021","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1161\/CIRCOUTCOMES.116.003039","article-title":"Analysis of machine learning techniques for heart failure readmissions","volume":"9","author":"Mortazavi","year":"2016","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1001\/jamacardio.2016.3956","article-title":"Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: Comparison of machine learning and other statistical approaches","volume":"2","author":"Frizzell","year":"2017","journal-title":"JAMA Cardiol."},{"key":"ref_20","first-page":"430","article-title":"Artificial Neural Network Model for Identifying Early Readmission of Diabetic Patients","volume":"8","author":"P","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10916-019-1243-3","article-title":"LSTM model for prediction of heart failure in big data","volume":"43","author":"Maragatham","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.compbiomed.2018.08.029","article-title":"Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology","volume":"101","author":"Reddy","year":"2018","journal-title":"Comput. Biol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vinzamuri, B., Li, Y., and Reddy, C.K. (2014, January 3\u20137). Active learning based survival regression for censored data. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai, China.","DOI":"10.1145\/2661829.2662065"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bussy, S., Veil, R., Looten, V., Burgun, A., Ga\u00efffas, S., Guilloux, A., Ranque, B., and Jannot, A.S. (2019). Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework. BMC Med. Res. Methodol., 19.","DOI":"10.1186\/s12874-019-0673-4"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e3137","DOI":"10.7717\/peerj.3137","article-title":"The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital","volume":"5","author":"Robinson","year":"2017","journal-title":"PeerJ"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e016921","DOI":"10.1136\/bmjopen-2017-016921","article-title":"Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: A retrospective cohort study","volume":"7","author":"Damery","year":"2017","journal-title":"BMJ Open"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"51","DOI":"10.2147\/CLEP.S149574","article-title":"Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease","volume":"10","author":"Hakim","year":"2018","journal-title":"Clin. Epidemiol."},{"key":"ref_28","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/S0893-6080(98)00010-0","article-title":"Automatic early stopping using cross validation: Quantifying the criteria","volume":"11","author":"Prechelt","year":"1998","journal-title":"Neural Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/S0169-2070(96)00719-4","article-title":"Testing the equality of prediction mean squared errors","volume":"13","author":"Harvey","year":"1997","journal-title":"Int. J. Forecast."},{"key":"ref_31","unstructured":"Rice, J.A. (2006). Mathematical Statistics and Data Analysis, Cengage Learning."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Freedman, D.A. (2009). Statistical Models: Theory and Practice, Cambridge University Press.","DOI":"10.1017\/CBO9780511815867"},{"key":"ref_33","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_34","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., and Vapnik, V. (1997). Support vector regression machines. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support vector regression. Efficient Learning Machines, Springer.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3389\/fict.2015.00006","article-title":"AWARE: Mobile context instrumentation framework","volume":"2","author":"Ferreira","year":"2015","journal-title":"Front. ICT"},{"key":"ref_37","unstructured":"Doryab, A., Chikarsel, P., Liu, X., and Dey, A.K. (2018). Extraction of behavioral features from smartphone and wearable data. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1016\/j.eswa.2013.09.005","article-title":"Facing the cold start problem in recommender systems","volume":"41","author":"Lika","year":"2014","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7510\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:29:00Z","timestamp":1760167740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7510"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227510"],"URL":"https:\/\/doi.org\/10.3390\/s21227510","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,11,12]]}}}