About me

Improving psychiatry and mental health care through observational health data.

I am an Assistant Professor in the Departments of Psychiatry and Computer Science at Johns Hopkins University. My work focuses on machine learning and statistical methods for using observational health data in psychiatry and mental health, especially dementia and suicide prevention. Much of this work is conducted as part of the Richman Family Precision Medicine Center of Excellence. Prior to this, I spent three years as a postdoctoral fellow in the Machine Learing and Healthcare lab and the Malone Center for Engineering in Healthcare. I received my PhD in 2018 from University of Massachusetts, Amherst working in the Machine Learning for Data Sciences lab with Prof. Benjamin Marlin.

Research interests

  • Machine learning for healthcare
  • Psychiatry and mental health
  • Causal inference
  • Measurement error in EHR data

Current projects

Thyroid disorders and dementia

A substantial body of literature has established a link between thyrotoxicosis (an excess of thyroid hormone) and dementia; however, the mechanism of this association remains unclear. Further, the large majority of thyrotoxicosis is caused by overtreatment with thyroid hormone rather than hyperthyroidism (overproduction of thyroid hormone), but patients on thyroid hormone have commonly been excluded from studies examining this link. Our initial work suggests that overtreatment with thyroid hormone may have a similar impact as hyperthyroidism and the main goal of this project is to further interrogate this link using a variety of observational data sources.

Endogenous and Exogenous Thyrotoxicosis and Risk of Incident Cognitive Disorders in Older Adults
Adams R, Oh ES, Yasar S, Lyketsos CG, Mammen JS
JAMA Internal Medicine (2023)

Suicide prevention in the Indian Health Service

Despite generations of research, our ability to identify individuals at risk of suicide and associated behaviors remains limited. Several large health systems, including the United States Veterans Administration (VA), Kaiser Permanente, and other academic medical institutions, have developed EHR-based risk models using statistical machine learning, which show promise. But none have been developed specifically for American Indian/Alaska Native populations who face the highest burden of suicide of any racial or ethnic group in the United States. In partnership with the White Mountain Apache Tribe and led by Dr. Emily Haroz in the Johns Hopkins Center for Indigenous Health, the goal of this project is to develop and deploy a suicide risk tool developed specifically for the Indian Health Service.

Developing a suicide risk model for use in the Indian Health Service
Adams R, Haroz EE, Rebman P, Suttle R, Grosvenor L, Bajaj M, Dayal RR, Maggio D, Kettering CL, Goklish N
npj Digital Medicine (2024)

Measurement error in EHR data

Data arising from real-world healthcare environments is affected by measurement error and missing data, observed and unobserved confounding, and a variety of societal biases. When reliable measurements are not available, it is critical that we account for systematic measurement error to avoid drawing biased inferences. Unfortunately, observational health data frequently violates the assumptions of classical measurement error analysis and we lack methods to incorporate many measurement error assumptions that commonly occur in healthcare settings. The goal of this project is to develop methods for estimating models from data with measurement error and to derive partial identification bounds when exact model identification is not possible.

Partial Identifiability in Discrete Data with Measurement Error
Finkelstein N, Adams R, Saria S, Shpitser I
Uncertainty in Artificial Intelligence (2021)

Joining the lab

Graduating PhD students interested in a potential postdoc in my group should email me their CV with the subject line Psych ML Postdoc. Aspiring PhD students interested in working with me should apply to the Computer Science department and list me in their application materials. Current JHU students interested in working with my group should email me their CV with the subject line Psych ML JHU.