Rapid diagnosis of fever etiology using wearable temperature monitoring and machine learning
Journal:
medRxiv
Published Date:
Jul 28, 2026
Abstract
Introduction. Distinct temperature patterns have long been recognized to correlate with fevers of differing etiologies. While the use of wearable sensors for high-frequency temperature monitoring (HFTM) on a near minute-by-minute basis has been shown to detect fevers earlier than standard-of-care nursing vital sign assessments in hospitalized patients, leveraging these high-resolution datasets to computationally identify unique digital signatures for real-time diagnosis of underlying fever etiology has not been widely explored. Diagnostic uncertainty is common in patients undergoing hematopoietic stem cell transplantation (HCT), with only 20-30% of febrile neutropenic episodes being microbiologically documented. We hypothesized that unique temperature patterns extracted from HFTM data collected during episodes of febrile neutropenia could be used to develop a supervised machine learning classifier capable of accurately predicting underlying fever etiology in HCT patients. Methods. We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq(R), BlueSpark Technologies) that measured axillary temperature every 2 minutes throughout hospitalization. Time-series features were extracted from temperature traces spanning 1 hour before to 3 hours after fever onset and used to train a suite of machine-learning models to distinguish engraftment fevers from other fever etiologies. Model training and evaluation were performed using repeated stratified 5-fold patient-level cross-validation, yielding 100 train-test evaluations. Results. Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84-1.0) for identifying engraftment fevers with a mean AUROC of 0.88 {+/-} 0.10. Feature importance analysis demonstrated that both clinical variables and HFTM-derived temperature dynamics contributed to model performance, with a strong reliance on time-series features captured within the first 4 hours of fever onset. Conclusion. Our study provides a demonstration that continuous temperature data collected from patients outfitted with wearable sensors can be leveraged not only for early fever detection but also for machine learning-based diagnosis of fever etiology. These findings suggest that dynamic temperature patterns contain clinically meaningful physiologic information that with further studies could support real-time diagnostic decision-making and guide safe de-escalation of empiric antibiotics during febrile neutropenia in patients undergoing intensive cancer therapy.
