Project Summary. Depression and anxiety are 2-4 times as likely prevalent among cardiovascular disease
(CVD) or diabetes mellitus (DM) patients than among those without CVD or DM. Co-morbid depression and
anxiety have a detrimental impact on CVD or DM patients, including exacerbating chronic symptoms and
increasing mortality. However, co-morbid depression and anxiety are often underdiagnosed due to the multi-
layer barriers at the patient, clinician, and health system levels. Particularly, symptomatic issues and care
needs for depression and anxiety might not be easily shared during cardiology or endocrinology visits while
clinicians focus on chronic physiological symptoms. The patient portal allows patients to communicate with
providers to share their symptoms and concerns, which may signal the early signs of depression and anxiety.
Recently introduced Large Language Model (LLM) algorithms have created a robust environment for extracting
meaningful topics from large text data. Moreover, machine learning (ML)-based risk models have been
designed to predict the risk of CVD or DM, yet, modeling to predict the risk of co-morbid depression and
anxiety has been remarkably rare. Thus, in Aim 1, Dr. Kim will identify symptomatic issues and care needs for
depression and anxiety among CVD or DM patients using patient portal messages. More than 46 million
messages from Stanford Health Center (SHC) will be analyzed by LLM algorithms. It will transform the raw text
data into groups of words, then weight them to generate salient topics which represent the primary symptoms
and care needs. The generative AI algorithm will enhance interpretability of the topics. In Aim 2, Dr. Kim will
develop co-morbid depression and anxiety risk prediction models and specify risk factors among CVD or DM
patients. She will leverage the Least Absolute Shrinkage and Selection Operator algorithm, using the electronic
health records of more than half a million patients at SHC to calculate the area under the curve to present the
accuracy of prediction and odds ratios with 95% confidence intervals to indicate the strength of risk factors.
The long-term goal is to apply this patient portal-based symptom detection and risk prediction approach to
other at-risk populations to prepare tailored interventions to ultimately improve depression and anxiety
outcomes, aligning with the mission of NIMH, "to transform the understanding and treatment of mental
illnesses, paving the way for prevention, recovery, and cure." The Career Development Plan will enable Dr.
Kim to gain hands-on skillsets to use the newest LLM packages and construct LASSO-based prediction
models independently, with an advanced understanding of the clinical context of mental disorders under the
guidance of mentors (Dr. Linos in Digital Health, Dr. Rodriguez in Psychiatry, Dr. Hernandez-Boussard in
Medical Informatics) and advisors in Biostatistics, Cardiology, Endocrinology, Bioethics. All in all, the strong
mentor team and solid training plans along with an excellent institutional support, will fully prepare Dr. Kim to
be a well-disciplined independent investigator in computational epidemiology and mental health.
