Improving antibiotic treatment decisions through machine learning

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 utility with randomized trials. The candidate's long term goal is to use these algorithms and the

infrastructure necessary to maintain them as the technologic basis, of a learning health system that provides personalized decision support at the provider and public health level.