Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data

Maya Stemmer, Lior Ungar, Talia Friedman, Lihi Bik, Yotam Hadari, Itamar Efrati, Yarden Rachamim, Lior Carmi, Shai Fine
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:443-457, 2025.

Abstract

This study followed patients suffering from chronic pain and aimed to predict their health states. To this end, we conducted a clinical study in which patients were digitally monitored via clinically validated questionnaires (SF-36 and EQ-5D) and continuously collected cellphone usage data. We present a novel two-step approach for utilizing the immense amounts of unlabeled cellular logs in a supervised, binary classification problem and predicting patient-reported outcomes from objective cellphone usage data. Reaching an accuracy of 0.827 for women and 0.898 for men, our classification results show the feasibility of using cellphone monitoring data for patients’ state prediction. Such a capability may enrich periodic clinical assessments with frequent digital follow-ups, assist in disease management for chronic patients, and raise awareness whenever necessary.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-stemmer25a, title = {Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data}, author = {Stemmer, Maya and Ungar, Lior and Friedman, Talia and Bik, Lihi and Hadari, Yotam and Efrati, Itamar and Rachamim, Yarden and Carmi, Lior and Fine, Shai}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {443--457}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/stemmer25a/stemmer25a.pdf}, url = {https://proceedings.mlr.press/v287/stemmer25a.html}, abstract = {This study followed patients suffering from chronic pain and aimed to predict their health states. To this end, we conducted a clinical study in which patients were digitally monitored via clinically validated questionnaires (SF-36 and EQ-5D) and continuously collected cellphone usage data. We present a novel two-step approach for utilizing the immense amounts of unlabeled cellular logs in a supervised, binary classification problem and predicting patient-reported outcomes from objective cellphone usage data. Reaching an accuracy of 0.827 for women and 0.898 for men, our classification results show the feasibility of using cellphone monitoring data for patients’ state prediction. Such a capability may enrich periodic clinical assessments with frequent digital follow-ups, assist in disease management for chronic patients, and raise awareness whenever necessary.} }
Endnote
%0 Conference Paper %T Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data %A Maya Stemmer %A Lior Ungar %A Talia Friedman %A Lihi Bik %A Yotam Hadari %A Itamar Efrati %A Yarden Rachamim %A Lior Carmi %A Shai Fine %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-stemmer25a %I PMLR %P 443--457 %U https://proceedings.mlr.press/v287/stemmer25a.html %V 287 %X This study followed patients suffering from chronic pain and aimed to predict their health states. To this end, we conducted a clinical study in which patients were digitally monitored via clinically validated questionnaires (SF-36 and EQ-5D) and continuously collected cellphone usage data. We present a novel two-step approach for utilizing the immense amounts of unlabeled cellular logs in a supervised, binary classification problem and predicting patient-reported outcomes from objective cellphone usage data. Reaching an accuracy of 0.827 for women and 0.898 for men, our classification results show the feasibility of using cellphone monitoring data for patients’ state prediction. Such a capability may enrich periodic clinical assessments with frequent digital follow-ups, assist in disease management for chronic patients, and raise awareness whenever necessary.
APA
Stemmer, M., Ungar, L., Friedman, T., Bik, L., Hadari, Y., Efrati, I., Rachamim, Y., Carmi, L. & Fine, S.. (2025). Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:443-457 Available from https://proceedings.mlr.press/v287/stemmer25a.html.

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