Amelia J. Averitt, MPH MA MPhil

Machine Learner. Data Scientist. Health Researcher. Doctoral Candidate.
Columbia University, Department of Biomedical Informatics.

About Me

Hello! I'm Amelia J. Averitt and I'm a quantitative health researcher with an interest in translating biomedical data into actionable knowledge through machine learning and data science. Check out my page to learn more about my work.

Based in New York City.

My Research

My interests include the application and invention of machine learning models to answer complex, real-world health problems. In my pre-doctoral position at Columbia University Medical Center, I work with Dr. Adler Perotte. Together, we develop novel deep-learning and probabilistic methods that support causal claims generated from observational electronic health record (EHR) data. Prior to my doctoral candidacy, I completed a master’s degree in Biostatistics and Epidemiology at Columbia University Mailman School of Public Health, where I studied under Dr. Stephen Morse.

Publications

Averitt AJ, Slovis BH, Tariq A, Vawdrey D, Perotte A. Characterizing the Opioid Epidemic Using Electronic Health Records. JAMIA Open. Nov 2018. link.

Averitt AJ, Natarajan K. Going Deep: The Role of Neural Networks for Renal Survival and Beyond. KI Reports. 2017. link.

Shust G, Jao J, Rodriguez-Caprio G, Posada R, Chen K, Averitt AJ, Sperling R. Salvage Regimens Containing Darunavir, Etravirine, Raltegravir, or Enfuvirtide in Highly Treatment-Experienced Perinatally Infected Pregnant Women. Journal of Pediatric Infectious Disease Society. 2013. link.

Slovis BH, Averitt AJ, Glassberg J, Lowry T Kuperman G, Shapiro J. 175 Hospital Crossover of Patients With Sickle Cell Disease in a Health Information Exchange. Annals of Emergency Medicine, Volume 68, Issue 4, S69. Oct 2016. link.

Buono J, Mathur K, Averitt AJ, Andrae DA. Economic Burden of Irritable Bowel Syndrome with Diarrhea: Retrospective Analysis of a U.S. Commercially Insured Population. JMCP. Nov 2016. link.

Averitt AJ. FTC Hold’s Roundtable Discussion Regarding Motor Vehicle Industry. FTC: Watch [Alexandria] 18 Oct. 2011, No.793 ed. Print.

Averitt AJ. Gilead Science vies for purchase of Pharmasset. FTC: Watch [Alexandria] 1 Dec. 2011, No. 796 ed. Print.

Podium Talks

Averitt AJ, Slovis BH, Tariq A, Vawdrey D, Perotte A. Characterizing the Urban Opioid Epidemic Using Electronic Health Records. American Medical Informatics (AMIA) Symposium. Washington DC. 2019. *Winner, Top 10 Video Abstract.

Averitt AJ. The Counterfactual Chi-GAN. OHDSI Stakeholder Meeting. 2019.

Averitt AJ. Machine Learning for Causal Inference Health. New York University (NYU) Machine Learning, Prof. Rajesh Ranganath. New York, NY. 2019.

Averitt AJ, Perotte AJ. Noisy-Or Risk Allocation for Causal Inference. American Medical Informatics (AMIA) Symposium. San Francisco, CA. 2018.

Averitt AJ, Weng C, Perotte AJ. Clinical Trial Eligibility Criteria Fail to Meet Burden of Generalizability. American Medical Informatics (AMIA) Symposium. Washington DC. 2017.

Slovis BH, Averitt AJ, Kuperman G, Glassberg T, Lowry T. Hospital Crossover of Patients with Sickle Cell Disease in a Health Information Exchange. ACEP. Las Vegas, NV. 2016.

Posters

Averitt AJ, Vanitchanant N, Ranganath R. Perotte A. The Counterfactual Chi-GAN. Observational Health and Data Science (OHDSI) Symposium. Bethesda, MD 2019. *Winner, Best Methodological Contribution. link.

Averitt AJ, Vanitchanant N, Ranganath R. Perotte A. The Counterfactual Chi-GAN. Atlantic Causal Inference Conference (ACIC). Montreal, Québec. 2019.

Averitt AJ, Vanitchanant N, Perotte A. The Counterfactual Chi-GAN. New York Academy of Science (NYAS) Machine Learning Symposium. New York, NY. 2019.

Averitt AJ, Perotte AJ. Noisy-Or Risk Allocation for Causal Inference. National Library of Medicine (NLM) Training Conference. Nashville, TN. 2018. *Winner, Best Poster Presentation.

Averitt AJ, Weng C, Perotte AJ. Do RCT Eligibility Criteria Identify Applicable Patients for Evidence-Based Medicine? Observational Health and Data Science (OHDSI) Symposium. Bethesda, MD. 2018.

Averitt AJ, Perotte AJ. Standardization of FDA Adverse Event Reporting System to the OHDSI Common Data Model. American Medical Informatics (AMIA) Symposium. Chicago, IL. 2016.

Buono J, Mathur K, Averitt AJ, Andrae DA. Economic Burden of Treatment Failure Among US Commercially Insured Patients with Irritable Bowel Syndrome with Diarrhea. AMCP Nexus. Orlando, FL. 2015.

Buono J, Mathur K, Averitt AJ, Andrae DA. Economic Burden of Irritable Bowel Syndrome With Diarrhea: Retrospective Analyses of a US Commercially Insured Population. American College of Gastroenterology. Honolulu, Hawaii. 2015.

Awards

Best Poster Presentation. Noisy-Or Risk Allocation for Causal Inference. NLM Informatics Training Conference. June 4-5, 2018. Nashville, TN.

Best Methodological Contribution.The Counterfactual Chi-GAN. OHDSI Sympoisum 2019. Sept 16, 2019. Bethesda, MD.

Top 10 Video Abstract. Characterizing the Urban Opioid Epidemic Using Electronic Health Records. American Medical Informatics (AMIA) Symposium. Nov 16-20, 2019. Washington, DC. link.

Contact

Feel free to send me an email me about collaborations, research and work opportunities, or to just say hello!

amelia.averitt@gmail.com
aja2149@cumc.columbia.edu