VG Vinod Vydiswaran

Accelerating Mixed Methods Research With Natural Language Processing of Big Text Data

Journal of Mixed Methods Research · 2021-06-16 · 64 citations

Situations of catastrophic social change, such as COVID-19, raise complex, interdisciplinary research questions that intersect health, education, economics, psychology, and social behavior and require mixed methods research. The pandemic has been a quickly evolving phenomenon, which pressures the time necessary to perform mixed methods research. Natural language processing (NLP) is a promising solution that leverages computational approaches to analyze textual data in “natural language.” The aim…

Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research

Journal of the American Medical Informatics Association · 2023-05-30 · 31 citations

OBJECTIVE: We performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care. MATERIALS AND METHODS: Starting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alo…

Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: <scp>State of the Art</scp>

Journal of the American Geriatrics Society · 2020 · 22 citations

Embedded pragmatic clinical trials (ePCTs) are embedded in healthcare systems as well as their data environments. For people living with dementia (PLWD), settings of care can be different from the general population and involve additional people whose information is also important. The ePCT designs have the opportunity to leverage data that becomes available through the normal delivery of care. They may be particularly valuable in Alzheimer's disease and Alzheimer's disease‐related dementia (AD/…

LIREx: Augmenting Language Inference with Relevant Explanations

Proceedings of the AAAI Conference on Artificial Intelligence · 2021-05-18 · 21 citations

Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasoning better, but not as beneficial for natural language inference (NLI). In this paper, we analyze two primary flaws in the way NLEs are currently used to train explanation generators…

Equity in virtual care: A mixed methods study of perspectives from physicians

Journal of Telemedicine and Telecare · 2023-08-28 · 17 citations

Background Virtual care expanded rapidly during the COVID-19 pandemic, and how this shift affected healthcare disparities among subgroups of patients is of concern. Racial and ethnic minorities, older adults, individuals with less education, and lower-income households have lower rates of home broadband, smartphone ownership, and patient portal adoption, which may directly affect access to virtual care. Because primary care is a major access point to healthcare, perspectives of primary care prov…