Bio

I am a fellow in Pediatric Infectious Diseases and Clinical Informatics, working to advance infectious diseases care through innovation and best practices. My research leverages machine learning to enhance clinical decision-making by delivering data-driven insights that optimize healthcare delivery and advance antimicrobial and diagnostic stewardship. Beyond my scholarly activities, I am passionate about medical education, quality improvement, and high-value care.

Clinical Focus

  • Fellow
  • Pediatric Infectious Diseases
  • Antimicrobial Stewardship
  • Immunocompromised Host
  • Clinical Informatics

Honors & Awards

  • Grant Recipient - Ernest and Amelia Gallo Endowed Fellow, Stanford Maternal & Child Health Research Institute (MCHRI) (07/2024)

Professional Education

  • Board Certification, American Board of Pediatrics, General Pediatrics (2023)
  • Chief Resident, Cleveland Clinic Children's, General Pediatrics (2023)
  • Resident, Cleveland Clinic Children's, General Pediatrics (2022)
  • MD, Northeast Ohio Medical University (NEOMED), Medicine (2019)
  • BS, The University of Akron, Natural Sciences and Chemistry (2014)

Additional Info

Lab Affiliations

Graduate and Fellowship Programs

All Publications

  • Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs. Scientific data Nateghi Haredasht, F., Amrollahi, F., Maddali, M. V., Marshall, N., Ma, S. P., Cooper, L. N., Johnson, A. O., Wei, Z., Medford, R. J., Kanjilal, S., Banaei, N., Deresinski, S., Goldstein, M. K., Asch, S. M., Chang, A., Chen, J. H. 2025; 12 (1): 1299

    Abstract

    The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.

    View details for DOI 10.1038/s41597-025-05649-7

    View details for PubMedID 40715119

    View details for PubMedCentralID PMC12297523

  • Antibiotic Resistance Microbiology Dataset (ARMD): A Resource for Antimicrobial Resistance from EHRs. ArXiv Haredasht, F. N., Amrollahi, F., Maddali, M. V., Marshall, N., Ma, S. P., Cooper, L. N., Johnson, A. O., Wei, Z., Medford, R. J., Kanjilal, S., Banaei, N., Deresinski, S., Goldstein, M. K., Asch, S. M., Chang, A., Chen, J. H. 2025

    Abstract

    The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.

    View details for DOI 10.48550/arXiv.2504.07278

    View details for PubMedID 40740524

    View details for PubMedCentralID PMC12310132

https://orcid.org/0009-0003-6051-5890