Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data
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
Text
Stemmer et al. "Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data." Proceedings of the sixth Conference on Health, Inference, and Learning, 2025.Markdown
[Stemmer et al. "Predicting Health States of Patients with Chronic Pain from Cellphone Usage Data." Proceedings of the sixth Conference on Health, Inference, and Learning, 2025.](https://mlanthology.org/chil/2025/stemmer2025chil-predicting/)BibTeX
@inproceedings{stemmer2025chil-predicting,
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},
year = {2025},
pages = {443-457},
volume = {287},
url = {https://mlanthology.org/chil/2025/stemmer2025chil-predicting/}
}