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Estimating the Treatment Effect of Antibiotics Exposure on the Risk of Developing Anti-Microbial Resistance
Hyewon Jeong · Kexin Yang · Ziming Wei · Yidan Ma · Intae Moon · Sanjat Kanjilal

Incorporating the propensity of an antibiotic engendering resistance to itself and other antibiotics is a potentially useful strategy for preventing antimicrobial resistance (AMR). However, prospective studies have been difficult to generalize to outpatients and retrospective studies are prone to design errors and model misspecification. To address this gap, we apply causal inference with Targeted Maximum Likelihood Estimation using machine learning, to data from the Electronic Health Record to define the antibiotic use (Treatment) - resistance (Outcome) relationship for common outpatient therapies used to treat urinary tract infection (UTI). We used total 20 covariates (Confounder) to adjust for the confounding effect of treatment on outcome. By estimating the treatment effect of antibiotics on future resistance, we expect to derive clinical correlation which helps build a decision support tool. Estimating the effect of antibiotic treatment will help clinicians design better care plans for patients by choosing the best antibiotics that minimize the risk of future AMR event.

Author Information

Hyewon Jeong (MIT)
Kexin Yang (Harvard School of Public Health)
Ziming Wei (Harvard School of Public Health)
Yidan Ma (Harvard School of Public Health)
Intae Moon (Massachusetts Institute of Technology)
Sanjat Kanjilal (Harvard Medical School)

Dr. Kanjilal is an Instructor in the Department of Population Medicine at the Harvard Pilgrim Health Care Institute and the Associate Medical Director of Clinical Microbiology at the Brigham & Women’s Hospital (BWH). He is also an infectious diseases physician at the BWH and the course director of HST 040, Mechanisms of Microbial Pathogenesis, at Harvard Medical School. Dr. Kanjilal's research interests focus on harnessing observational and experimental data to improve the diagnosis and management of infectious diseases. Specific areas of work include the development of decision support tools built over machine learning models that assist healthcare providers in a variety of tasks such as antibiotic treatments and diagnostic testing strategies. His long term goal is to improve medical decision making by integrating accurate and interpretable artificial intelligence-supported meta-diagnostics into clinical workflows and to use these models on a broader scale to inform public health policy.

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