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

  • Machine Learning-Based Prediction of Antimicrobial Susceptibility: A Step Towards Precision Antimicrobial Stewardship. AMIA ... Annual Symposium proceedings. AMIA Symposium Amrollahi, F., Haredasht, F. N., Vansomphone, A., Marshall, N., Maddali, M. V., Ma, S. P., Chang, A., Deresinski, S. C., Goldstein, M. K., Kanjilal, S., Medford, R. J., Cooper, L. N., Asch, S. M., Banaei, N., Chen, J. H. 2024; 2024: 138-146

    Abstract

    Antimicrobial resistance (AMR) represents an urgent global health crisis exacerbated by the frequent empirical use of broad-spectrum antibiotics. AMR is exacerbated by inherent delays in obtaining culture results and antimicrobial susceptibility data after sample collection. In this study, we developed and validated Machine Learning (ML) models using routinely collected EHR data from inpatient and outpatient encounters to predict antibiotic resistance at the time of blood, urine or respiratory bacterial culture collection. The models demonstrated robust predictive accuracy, particularly in inpatient settings where clinical data was more consistently available. Notably, the model independently identified patterns that predict resistance, similar to how a clinician would attempt to predict resistance using prior culture and susceptibility data combined with their clinical training and knowledge of microbiological resistance patterns. Integrating these predictive tools into clinical workflows could significantly enhance empirical antibiotic selection, reduce unnecessary broad-spectrum antibiotic use, and meaningfully advance antimicrobial stewardship efforts.

    View details for DOI 10.1023/A:1012487302797

    View details for PubMedID 41726396

    View details for PubMedCentralID PMC12919605

  • A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation. AMIA ... Annual Symposium proceedings. AMIA Symposium Amrollahi, F., Marshall, N., Haredasht, F. N., Black, K. C., Zahedivash, A., Maddali, M. V., Ma, S. P., Chang, A., Deresinski, S. C., Goldstein, M. K., Asch, S. M., Banaei, N., Chen, J. H. 2024; 2024: 147-156

    Abstract

    Blood cultures are often overordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use-pressures worsened by the global shortage. In the study of 135,483 emergency department (ED) blood culture orders, we developed machine learning (ML) models to predict the risk of bacteremia using structured electronic health record (EHR) data and provider notes via a large language model (LLM). The structured model's AUC improved from 0.76 to 0.79 with note embeddings and reached 0.81 with added diagnosis codes. Compared to an expert recommendation framework applied by human reviewers and an LLM-based pipeline, our ML approach offers higher specificity without compromising sensitivity. The recommendation framework achieved sensitivity 86%, specificity 57%, while the LLM maintained high sensitivity (96%) but overclassified negatives, reducing specificity (16%). These findings demonstrate that ML models integrating structured and unstructured data can outperform consensus recommendations, enhancing diagnostic stewardship beyond existing standards of care.

    View details for PubMedID 41726397

    View details for PubMedCentralID PMC12919529

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