# Improving antibiotic treatment decisions through machine learning

> **NIH AHRQ K08** · HARVARD PILGRIM HEALTH CARE, INC. · 2021 · $149,505

## Abstract

PROJECT SUMMARY / ABSTRACT
Infections from antibiotic resistant bacteria represent one of the biggest challenges facing modern medical care.
Suboptimal antibiotic use is one of the major drivers for antibiotic resistance, however clinicians lack robust tools
to help them make rational treatment decisions at the patient-level. The goal of this 3-year mentored clinical
scientist research career development program is to apply state-of-the-art machine learning models to routinely
collected data in the electronic health record to predict the risk of antimicrobial resistance (AMR) prior to, and
after antibiotic treatment. The candidate, Dr. Sanjat Kanjilal, has identified two important clinical problems where
improved risk prediction for AMR could have a significant impact on quality of care. The first is the overuse of
broad-spectrum antibiotics in patients presenting with community-onset sepsis. To address this, the candidate
will develop a set of machine learning prediction models trained on routinely collected data in the electronic
health record to help clinicians identify which antibiotic(s) will effectively treat the patient's pathogen while being
of the narrowest possible spectrum. The second problem is the inability to assess the risk of a patient developing
an antibiotic resistant infection after being treated with an antibiotic. The candidate proposes to build a robust
causal inference model using targeted maximum likelihood estimation combined with machine learning to
estimate the impact of taking various commonly used outpatient antibiotics on the risk of developing a drug
resistant infection in the 12 month period after treatment. The results of this work will form the basis of a precision
medicine approach to antibiotic stewardship and treatment.
The candidate is a practicing infectious diseases clinician and the Associate Medical Director of the clinical
microbiology laboratory at the Brigham & Women's Hospital. He has prior experience in building machine
learning algorithms that provide robust antimicrobial stewardship. His unique background combined with the rich
supporting environment of the Department of Population Medicine at Harvard Medical School and Harvard
Pilgrim Health Care Institute, position him well for the transition to becoming an independently funded clinician-
scientist working at the interface of infectious diseases and machine learning. He has assembled a
multidisciplinary mentorship team consisting of experts in sepsis epidemiology, antimicrobial stewardship,
implementation science, machine learning and causal inference to help him achieve his goals and has identified
a comprehensive training plan that provides him the skills necessary to become a leader in his field. His short
term goal is to become an expert in the development of machine learning algorithms that improve decision
making for antibiotic resistant infections. His medium term goal is to deploy these models at scale and evaluate
their real-world utilit...

## Key facts

- **NIH application ID:** 10301631
- **Project number:** 1K08HS027841-01A1
- **Recipient organization:** HARVARD PILGRIM HEALTH CARE, INC.
- **Principal Investigator:** Sanjat Kanjilal
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $149,505
- **Award type:** 1
- **Project period:** 2021-07-02 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10301631

## Citation

> US National Institutes of Health, RePORTER application 10301631, Improving antibiotic treatment decisions through machine learning (1K08HS027841-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10301631. Licensed CC0.

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