{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T13:00:38Z","timestamp":1773666038841,"version":"3.50.1"},"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:00:00Z","timestamp":1615420800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01AT010413-01"],"award-info":[{"award-number":["1R01AT010413-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient\u2019s subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients\u2019 pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008542","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T18:25:44Z","timestamp":1615487144000},"page":"e1008542","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1790-5899","authenticated-orcid":true,"given":"Mark J.","family":"Panaggio","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6015-8358","authenticated-orcid":true,"given":"Daniel M.","family":"Abrams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7550-0662","authenticated-orcid":true,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9794-3755","authenticated-orcid":true,"given":"Tanvi","family":"Banerjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7506-0935","authenticated-orcid":true,"given":"Nirmish R.","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"issue":"2","key":"pcbi.1008542.ref001","doi-asserted-by":"crossref","first-page":"311","DOI":"10.5811\/westjem.2017.9.35422","article-title":"Emergency Department (ED), ED Observation, Day Hospital, and Hospital Admissions for Adults with Sickle Cell Disease","volume":"19","author":"DM Cline","year":"2018","journal-title":"Western Journal of Emergency Medicine"},{"issue":"4","key":"pcbi.1008542.ref002","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1002\/nur.4770130405","article-title":"A critical review of visual analogue scales in the measurement of clinical phenomena","volume":"13","author":"ME Wewers","year":"1990","journal-title":"Research in Nursing & Health"},{"key":"pcbi.1008542.ref003","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"P Hamet","year":"2017","journal-title":"Metabolism"},{"issue":"4","key":"pcbi.1008542.ref004","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1097\/j.pain.0000000000001118","article-title":"Machine learning in pain research","volume":"159","author":"J L\u00f6tsch","year":"2018","journal-title":"Pain"},{"issue":"10","key":"pcbi.1008542.ref005","doi-asserted-by":"crossref","first-page":"e0140330","DOI":"10.1371\/journal.pone.0140330","article-title":"Pain intensity recognition rates via biopotential feature patterns with support vector machines","volume":"10","author":"S Gruss","year":"2015","journal-title":"PloS one"},{"key":"pcbi.1008542.ref006","doi-asserted-by":"crossref","unstructured":"Lopez-Martinez D, Picard R. 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IEEE; 2011. p. 4737\u20134740."},{"key":"pcbi.1008542.ref012","doi-asserted-by":"crossref","unstructured":"Olugbade TA, Bianchi-Berthouze N, Marquardt N, Williams AC. Pain level recognition using kinematics and muscle activity for physical rehabilitation in chronic pain. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE; 2015. p. 243\u2013249.","DOI":"10.1109\/ACII.2015.7344578"},{"key":"pcbi.1008542.ref013","doi-asserted-by":"crossref","unstructured":"Werner P, Al-Hamadi A, Niese R, Walter S, Gruss S, Traue HC. Automatic pain recognition from video and biomedical signals. In: 2014 22nd International Conference on Pattern Recognition. IEEE; 2014. p. 4582\u20134587.","DOI":"10.1109\/ICPR.2014.784"},{"key":"pcbi.1008542.ref014","doi-asserted-by":"crossref","unstructured":"Zamzmi G, Pai CY, Goldgof D, Kasturi R, Ashmeade T, Sun Y. An approach for automated multimodal analysis of infants\u2019 pain. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE; 2016. p. 4148\u20134153.","DOI":"10.1109\/ICPR.2016.7900284"},{"key":"pcbi.1008542.ref015","doi-asserted-by":"crossref","unstructured":"Ferri C, Hern\u00e1ndez-orallo J, Salido MA. Volume Under the ROC Surface for Multi-class Problems. Exact Computation and Evaluation of Approximations. In: Proc. of 14th European Conference on Machine Learning; 2003. p. 108\u2013120.","DOI":"10.1007\/978-3-540-39857-8_12"},{"issue":"5","key":"pcbi.1008542.ref016","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TMI.2007.908687","article-title":"The Meaning and Use of the Volume Under a Three-Class ROC Surface (VUS)","volume":"27","author":"X He","year":"2008","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"pcbi.1008542.ref017","doi-asserted-by":"crossref","unstructured":"Yang F, Banerjee T, Panaggio MJ, Abrams DM, Shah N. Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE; 2019.","DOI":"10.1109\/BIBM47256.2019.8983282"},{"key":"pcbi.1008542.ref018","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.smhl.2018.01.002","article-title":"Improving pain management in patients with sickle cell disease from physiological measures using machine learning techniques","volume":"7-8","author":"F Yang","year":"2018","journal-title":"Smart Health"},{"key":"pcbi.1008542.ref019","volume-title":"Multiple imputation for nonresponse in surveys","author":"DB Rubin","year":"2004"},{"key":"pcbi.1008542.ref020","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-52452-8","volume-title":"Time series analysis and its applications: with R examples","author":"RH Shumway","year":"2017"},{"issue":"2","key":"pcbi.1008542.ref021","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-Validatory Choice and Assessment of Statistical Predictions","volume":"36","author":"M Stone","year":"1974","journal-title":"Journal of the Royal Statistical Society Series B (Methodological)"},{"key":"pcbi.1008542.ref022","unstructured":"Domingos P, Pazzani M. Beyond independence: Conditions for the optimality of the simple bayesian classifier. In: Proc. 13th Intl. Conf. Machine Learning; 1996. p. 105\u2013112."},{"issue":"1","key":"pcbi.1008542.ref023","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"LE Baum","year":"1970","journal-title":"The annals of mathematical statistics"},{"issue":"1","key":"pcbi.1008542.ref024","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MASSP.1986.1165342","article-title":"An introduction to hidden Markov models","volume":"3","author":"L Rabiner","year":"1986","journal-title":"IEEE ASSP Magazine"},{"issue":"2","key":"pcbi.1008542.ref025","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TIT.1967.1054010","article-title":"Error bounds for convolutional codes and an asymptotically optimum decoding algorithm","volume":"13","author":"A Viterbi","year":"1967","journal-title":"IEEE Transactions on Information Theory"},{"issue":"3","key":"pcbi.1008542.ref026","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/PROC.1973.9030","article-title":"The viterbi algorithm","volume":"61","author":"GD Forney","year":"1973","journal-title":"Proceedings of the IEEE"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008542","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T18:26:48Z","timestamp":1615487208000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008542"}},"subtitle":[],"editor":[{"given":"George Em","family":"Karniadakis","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,11]]},"references-count":26,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1008542","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,11]]}}}