BD-STEP Fellows at VA Palo Alto Health Care System
Current VA Palo Alto BD-STEP Fellows
Joanna Lankester, PhD. Dr. Lankester has a PhD in Electrical Engineering from Stanford University.
Inferring causal effect of risk factors on chronic diseases using genetics. Observational data is widely available but allows only for correlative analysis, while randomized trials give causal information but are not possible for many risk factors. Using genetic instrument variables, we can use observational datasets to infer causality of risk factors on chronic diseases, e.g., oncologic and cardiovascular outcomes.
Heather Selby, PhD, MS. Dr. Selby has a PhD in Bioinformatics from Boston University and an MS in Bioinformatics from Boston University.
Radiogenomics in Lung Cancer. The focus of my research in the Gevaert Lab is the biomedical data fusion of lung cancer using radiogenomics. The objective of my radiogenomics research is to develop a framework for non-invasive personalized medicine.
Raymond Van Cleve, PhD, MS. Dr. Van Cleve has a PhD in Health Services Research from the University of Pittsburgh and an MS in Information Systems from Robert Morris University.
Disruptions in care as a result of COVID-19. I am learning how to leverage VA data to better understand trends in access to care and how the VA is improving and delivering care. I am focusing on how the COVID-19 Pandemic disrupted care and how different VA programs are engaging and re-engaging veterans. I am interested broadly in program evaluation, including palliative care and LGBTQ+ health. I hope that by the end of my fellowship I can leverage some of the geographic data to better understand how certain trends spread across the US.
Former VA Palo Alto BD-STEP Fellows
Jacqueline Ferguson, MS, PhD. Dr. Ferguson has a doctoral degree in Environmental Health Sciences from the University of California, Berkeley and a Masters of Health Science in Environmental Health from the Johns Hopkins Bloomberg School of Public Health.
Veterans Negatively Affected by Multiple Social Determinants of Health: Identifying Important Components and Estimating Mixture Effects.As a first year BD-STEP fellow, Jacqueline is applying methodology, primarily developed for assessing chemical mixtures in environmental epidemiology, to examine co-occurring social determinants of health. Her research seeks to understand how multiple social determinants of health can simultaneously influence veteran care and health within the Veterans Health Administration.
Hoda Abdel Magid, MHS, PhD. Dr. Abdel Magid has a doctoral degree in Epidemiology from the University of California, Berkeley and a Masters of Health Science in Environmental Epidemiology from the Johns Hopkins Bloomberg School of Public Health.
Assessing Spatial Variability in Telemedicine Utilization among Multiple Sclerosis Patients in the Veterans Health Administration: Multiple Sclerosis (MS) is the most common progressive neurological condition of young adults affecting over two million persons worldwide. With the advent of the high speed internet, inexpensive cameras and monitoring software, telemedicine has shown promise in bridging the gap between providers and their patients who have limited access to MS specialty care. The overall objective of this study is to use large national electronic administrative databases to determine country-wide and regional utilization patterns of telehealth in patients with MS. Using geographic information systems can reveal the spatial reach of telemedicine programs among MS patients and inform future expansion.
Linda (Diem) Tran, MPP, PhD. Dr. Tran has a Master of Public Policy degree and a PhD in Health Policy and Management from UCLA.
Continuity of Cancer Care as VA Expands Access: This project examines coordination of cancer treatment and quality of care as veterans receive more services in the community. Potential quality of care metrics include timely surveillance, documentation of treatment plans, and duplication of services.
Andrew Chang, MS, PhD. Dr. Chang has a MS in Biomedical Informatics from the University of Texas Health Science Center at Houston and a PhD in Biochemistry and Cell Biology from Rice University.
Using machine learning and big-data for better disease prediction and prevention: By combining the machine learning technology and the tremendous HER database of the VA system, we can search for possible patterns and factors of these fast-developing cancers that were previously ignored or neglected by humans.
Steven Cogill, MS, PhD. Dr. Cogill has a PhD in Genetics/Bioinformatics from Clemson University and a MS in Biochemistry/Biotech from IU School of Medicine.
Data mining techniques for the identification of post chemotherapy sepsis risk: Given that sepsis accounts for ~10% of cancer deaths in the US, particularly for post chemotherapy patients with neutropenic fever, better sepsis diagnostic/prognostic tool would enable improved sepsis management in cancer patients. We will mine available VA data on cancer patients with ED visits to identify predictors for various clinical or operational outcomes using novel machine-learning approaches.
Kent Heberer, MS, PhD. Dr. Heberer has a MS and PhD in Biomedical Engineering from UCLA.
Clinical predictors of outcomes to cancer treatments: Predictive analytics and existing natural language processing tools will be used to identify clinical predictors for treatment outcomes. These outcomes include treatment response, side effects and complications, remission/NED, quality of life and frailty measures, and survival. Eventually, the intent is to construct a core pipeline framework that can be customized and scalable for any cancer type, and be updated with the entry of new patients. Importantly, this pipeline can be used for future research and clinical operations in real-time to inform actionable strategies for the local, regional, and national levels of the VA healthcare system.
Joanna Sylman, PhD. Dr. Sylman has a PhD in Chemical and Biomedical Engineering from Oregon Health and Science University.
Utilization of longitudinal complete blood count information to improve cancer patient detection and prognosis: This project focuses on leveraging longitudinal complete blood count information, particularly platelets, neutrophils, lymphocytes, and albumin in an effort to detect cancer in early stages and improve patient prognosis predictions. Machine learning approaches and traditional epidemiological methods will be compared to determine the potential added value of these readily available measurements in Veteran patient medical health records.
Wen-Wai Yim, PhD. Dr. Yim has a PhD in Biomedical Informatics from the University of Washington.
Development and validation of surgery and pain-related quality metrics: Using structured and unstructured VA data to study outcomes and develop/evaluate quality metrics for quality improvement.
Lesley Park, MPH, MPhil, PhD. Dr. Park has a MPH in Chronic Disease Epidemiology, a MPhil in Epidemiology, and a PhD in Chronic Disease Epidemiology from Yale University.
The National BD-STEP program has compiled a list of publications by all BD-STEP Fellows during their time in the BD-STEP program: https://cssi.cancer.gov/about-us/cssi-offices/office-director/pilot-programs/big-data-scientist-training-enhancement/selected-fellow-publications
Last updated: 2022-04-01