Clinical Focus

  • Clinical Psychology

Academic Appointments

Professional Education

  • Fellowship: Childrens National Medical Center (2013) DC
  • Internship: Kennedy Krieger Institute Dept of Behavioral Psychology (2012) MD
  • PhD Training: University of Wisconsin Milwaukee Registrar (2012) WI

Contact

  • Academic
    ksalamon@stanford.edu
    Department: Anesthesia - Pediatric Position: Clinical Associate Professor
  • Clinical (Primary)  Pediatric Pain Management 321 Middlefield Rd Ste 225 Menlo Park, CA 94025 (650) 725-0009 (fax)

Additional Clinical Info

Additional Info

All Publications

  • East Coast to West Coast: Exploring Referral Patterns to Interdisciplinary Pediatric Pain Programs Salamon, K., Hildenbrand, A. OXFORD UNIV PRESS INC. 2026: 108

    View details for Web of Science ID 001744393400156

  • A natural language processing pipeline for identifying pediatric long COVID symptoms and functional impacts in freeform clinical notes: a RECOVER study. JAMIA open Bunnell, H. T., Reedy, C., Lorman, V., Jhaveri, R., Rivera-Sepulveda, A., Salamon, K. S., Patel, P. B., Morse, K. E., Davenport, M. A., Cowell, L. G., Utidjian, L., Christakis, D. A., Rao, S., Sills, M. R., Case, A., Mendonca, E. A., Taylor, B. W., Rutter, J., Martinez, A. T., Letts, R., Bailey, L. C., Forrest, C. B. 2025; 8 (5): ooaf089

    Abstract

    To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children.We analyzed 48 287 outpatient progress notes from 10 618 pediatric patients from 12 institutions. We evaluated notes obtained 28 to 179 days after a COVID-19 diagnosis or positive test. Two samples were examined: patients with evidence of Long COVID and patients with acute COVID but no evidence of Long COVID based on diagnostic codes. The pipeline identified clinical concepts associated with 21 symptoms and 4 functional impact categories. Subject matter experts (SMEs) screened a sample of 4586 terms from the NLP output to assess pipeline accuracy. Prevalence and concordance of each of the 25 concepts was compared between the 2 patient samples.A binary assertion measure comparing SME and NLP assertions showed moderate accuracy (N = 4133; F1 = .80) and improved substantially when only high-confidence SME assertions were considered (N = 2043; F1 = .90). Overall, the 25 Long COVID concept categories were markedly more prevalent in the presumptive Long COVID cohort, and differences were noted between concepts identified in notes versus structured data.This preliminary analysis illustrates the additional insight into a syndrome such as Long COVID gained from incorporating notes data, characterizing symptoms and functional impacts.These data support the importance of incorporating NLP methodology when possible into designing computable phenotypes and to accurately characterize patients with Long COVID.

    View details for DOI 10.1093/jamiaopen/ooaf089

    View details for PubMedID 40918941

    View details for PubMedCentralID PMC12409404

  • Increasing Accessibility to Integrated Pain Treatment: Analyzing Racial Trends of Referrals Compared to New Patient Intakes Macias, M., Flagg, A., Salamon, K. OXFORD UNIV PRESS INC. 2025: 119

    View details for Web of Science ID 001434801700248

https://orcid.org/0000-0003-0372-7670