{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T09:58:26Z","timestamp":1785491906679,"version":"3.56.0"},"posted":{"date-parts":[[2026,7,28]]},"group-title":"Health Informatics","reference-count":27,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>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\u201330% 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We analyzed 68 clinically independent fever episodes recorded in HCT patients (n=90) outfitted with an FDA-cleared wireless temperature sensor (TempTraq\u00ae, 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Among all classification models, the logistic regression classifier provided the best overall performance and interpretability, achieving 94% specificity (95% CI, 0.84\u20131.0) for identifying engraftment fevers with a mean AUROC of 0.88 \u00b1 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>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\u2013based 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.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.07.27.26359010","type":"posted-content","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T23:05:13Z","timestamp":1785279913000},"source":"Crossref","is-referenced-by-count":0,"title":["Rapid diagnosis of fever etiology using wearable temperature monitoring and machine learning"],"prefix":"10.64898","author":[{"given":"Shihan N.","family":"Khan","sequence":"first","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiheng","family":"Ren","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Wittrup","sequence":"additional","affiliation":[{"name":"Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmi","family":"Madhukar","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Flora","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly","family":"Mayhew","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle","family":"Rozwadowski","sequence":"additional","affiliation":[{"name":"Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Winnega","sequence":"additional","affiliation":[{"name":"Division of Pediatric Hematology and Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kay","family":"Leopold","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason B.","family":"Weinberg","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Division of Infectious Diseases, University of Michigan, Ann Arbor, Michigan, USA"},{"name":"Department of Microbiology & Immunology, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Paludo","sequence":"additional","affiliation":[{"name":"Division of Hematology, Mayo Clinic, Rochester, MN"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam F.","family":"Binder","sequence":"additional","affiliation":[{"name":"Thomas Jefferson University Hospital, Philadelphia, PA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monalisa","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Frame","sequence":"additional","affiliation":[{"name":"Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin","family":"Craig","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas M.","family":"Braun","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishi","family":"Chanderraj","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony D.","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Hematologic Malignancies & Cellular Therapeutics, University of Kansas Medical Center, Kansas City, KS, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[{"name":"Gilbert S. 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