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Chair’s Message

pth-use-this-oneWe are moving toward our vision with a number of activities across our various programs. We have updated our strategic plan in response to the 10-year academic program review that we recently completed. For our research-oriented MS and PhD programs, we have recently added a specialization in Data Science. We are completing a curriculum revision for our on line applied clinical informatics MS which will be effective Fall 2020. The work of our fellows in the clinical informatics fellowship program has received plaudits from clinical administrators and faculty, and we are currently recruiting a new faculty member in our department to assist with this program (view position description).  We are also recruiting a faculty member in medical education to start Summer 2020 (view position description). This is the beginning of a new cycle of admissions to our graduate programs, and we look forward to another productive year, and new growth in our department.

Cordially,

Peter Tarczy-Hornoch, MD
Chair and Professor, Department of Biomedical Informatics and Medical Education

Biomedical Informatics and Medical Education Newsletter

May 20 – May 24, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Walter H. Curioso, M.D., Ph.D., M.P.H.
Thursday, May 30th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will be in-person

Title: Telehealth for Strengthening Health Systems in Low- and Middle-income Countries: A Perspective from Peru
Abstract: The COVID-19 pandemic underscored the critical need for robust infrastructure and strengthened health information systems in low- and middle-income countries. Telehealth has the potential to significantly improve access to care, especially in rural and hard-to-reach areas. However, barriers such as the digital divide, privacy concerns, and resistance from healthcare professionals need to be addressed.
The pandemic prompted Peru to rapidly adjust its legal framework to adopt telemedicine and promote telehealth services, addressing urgent healthcare needs. This talk will review the regulatory changes and highlight key telehealth initiatives that emerged in Peru during the pandemic. The evolution of the Peruvian telehealth framework began in 2005, with subsequent laws aimed at establishing a national telehealth network. Despite these efforts, challenges remain, such as inadequate infrastructure in healthcare centers, limited high-speed Internet connectivity, and the need for better interoperability of health information systems. Telehealth represents a transformative approach to enhancing health systems in Peru and other low- and middle-income countries, with the potential to improve public health services beyond the pandemic.

Speaker Bio: Dr. Curioso holds a Ph.D. in Biomedical Informatics from the University of Washington (UW), Seattle, United States, as well as an M.P.H. from UW, and received his M.D. from Universidad Peruana Cayetano Heredia in Peru. Over the past 20 years, Dr. Curioso has held numerous leadership positions at both national and international levels, spanning the public and private sectors. He was elected a member of the World Health Organization Digital Health Roster of Experts. In academia, he is an Affiliate Associate Professor in the Department of Biomedical Informatics and Medical Education in the School of Medicine at the University of Washington, Seattle, Washington, USA. Dr. Curioso is currently the Vice Provost for Research at Universidad Continental in Peru. He is a founding associate editor of Oxford Open Digital Health and a member of the editorial board of the journals International Journal of Medical Informatics, Informatics for Health and Social Care, and Population Health Metrics. Dr. Curioso has recently been elected to the 2024 class of Academy Fellows of the International Academy of Health Sciences Informatics. His full bio and publications can be found here.

PAPERS & PRESENTATIONS
Wei Q, Mease PJ, Chiorean M, Iles-Shih L, Matos WF, Baumgartner A, Molani S, Hwang YM, Belhu B, Ralevski A, Hadlock J. Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system. Lancet Digit Health. 2024 May, 10.1016/S2589-7500(24)00021-9.

UPCOMING EXAMS
Final Exams

Title: Making Health Knowledge Accessible Through Personalized Language Processing

Student: Yue Guo
Date/Time: Thursday, May 30, 2 pm
Location: C123 A/B
Zoom: https://washington.zoom.us/my/cohenta

Abstract: The pandemic exposed the difficulties the general public faces when attempting to use scientific information to guide their health-related decisions. Though widely available in scientific papers, the information required to guide these decisions is often not accessible: medical jargon, scientific writing styles, and insufficient background explanations make this information opaque to non-experts. Consequently, there is a pressing need to deliver scientific knowledge in lay language, which has motivated researches on automated plain language summary generation to make the health information more accessible.

In this talk, I will discuss my efforts in this direction, including building a novel dataset, identifying unique challenges within this task, and developing new methods to address those challenges. A key part of this process has been evaluating existing metrics to see if they effectively measure performance for this task, and considering if there might be better options. Finally, I will broaden the discussion beyond just health information – exploring how we can personalize and improve communication across different domains.

Title: Relationship between Metabolic Measurements and Brain Injury in Post-Cardiac Arrest Comatose Patients
Student: Kevin Chen
Date/Time: Wednesday, June 5, 9 AM
Location-Zoom only: https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09

Abstract:
Cardiac arrest is a leading cause of death in the United States contributing to 5.6% of annual deaths. More than 80% of survivors are in permanent coma and 50-80% of those will die. Despite advancements in cardiac care, the mechanisms underlying brain injury are complex and not well understood, which has limited advancement in brain targeted therapies. Specifically, the relationship between regional brain metabolism and risk of brain injury is not known. This thesis investigated the relationship between brain injury in post-cardiac arrest comatose patients and metabolic characteristics: cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral metabolic rate of oxygen (CMRO2), and cerebral metabolic rate of glucose (CMRglu). The study analyzed whole brains, brains clustered by injury percentages, and brain regions clustered by injury percentage. Resulting correlations showed that CMRO2 and CMRglu had stronger correlations with brain injury than CBF and CBV, indicating a closer link between oxygen and glucose utilization and brain damage. Patients with minimal injury exhibited weak correlations, while patients with moderate to severe injuries displayed stronger correlations, emphasizing the critical role of oxygen and glucose metabolism in brain damage progression.

General Exam
Title: Enhancing Generalizability and Explainability in Medical Language and Vision Models
Student: Yujuan Velvin Fu
Date/Time: Thursday, June 6, 2 PM (Pacific)
Location: C123 A&B
Zoom: https://washington.zoom.us/my/melihay

Abstract:
Recent advances in large language models (LLMs) and vision-language models (VLMs) have achieved human-comparable performance across a variety of specialized medical tasks involving text and multimodal data, without requiring annotated datasets for model development. Despite these achievements, our observations identify two primary gaps: firstly, the generalization of current medical LLMs to some natural language understanding (NLU) tasks is suboptimal under zero-shot settings, compared to traditional fine-tuning-based approaches; secondly, the current generalizable and explainable VLMs in dermatology in under-explored for fine-grained diagnostic concept explanation. Addressing these challenges is critical, as enhancing model generalizability ensures quality and facilitates broader adoption of clinical LLMs and VLMs under potential patient distribution shifts, while improving explainability is essential for debugging AI-driven decision-making systems and fostering trust between humans and AI. Therefore, our work aims to tackle these issues through three major aims: (1) developing benchmark datasets for clinical information extraction (IE), as a subset of NLU, to more accurately identify the shortcomings of zero-shot LLMs; (2) developing a more generalizable medical NLU model through instruction fine-tuning and knowledge distillation, and evaluating it on the dataset developed in aim (1); and (3) developing more generalizable and explainable dermatology VLM through open-vocabulary concept-based image segmentation and object detection. Together, we hope to enhance the generalizability and explainability of medical language and vision models.

ANNOUNCEMENTS
Dr. Janice Sabin will lead a workshop, as a member of the Women’s National Health Forum Working Group, National Academies of Sciences, Engineering, and Medicine, Health & Medicine Division, title: Understanding micro-aggression and implicit bias awareness among providers and educators in maternal health. The workshop is #4 of a 5-part series, June 14, 2024. Panelists: Janice A. Sabin, PhD, MSW, Mary Owen, MD, Lauren Ramos, MPH. https://www.nationalacademies.org/our-work/maternal-health-disparities-the-women-behind-the-data-a-webinar-series

May 6 – May 10, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Yue Guo, M.B.B.S., MHS
Thursday, May 16th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Making Health Knowledge Accessible Through Personalized Language Processing

Abstract: The pandemic exposed the difficulties the general public faces when attempting to use scientific information to guide their health-related decisions. Though widely available in scientific papers, the information required to guide these decisions is often not accessible: medical jargon, scientific writing styles, and insufficient background explanations make this information opaque to non-experts. Consequently, there is a pressing need to deliver scientific knowledge in lay language, which has motivated researches on automated plain language summary generation to make the health information more accessible.

In this talk, I will discuss my efforts in this direction, including building a novel dataset, identifying unique challenges within this task, and developing new methods to address those challenges. A key part of this process has been evaluating existing metrics to see if they effectively measure performance for this task, and considering if there might be better options. Finally, I will broaden the discussion beyond just health information – exploring how we can personalize and improve communication across different domains.

Speaker Bio: Yue Guo is a physician-scientist and doctoral candidate at the University of Washington, Seattle, where she is pursuing a Ph.D. in Biomedical and Health Informatics. Her unique background, which includes an M.B.B.S (equivalent to an M.D.) from Capital Medical University in China, a master’s in epidemiology from Johns Hopkins University, and postdoctoral research experience in Radiation Oncology and Molecular Radiation Sciences at the Johns Hopkins University School of Medicine, allows her to bridge the gap between clinical medicine and informatics research.

Dr. Guo’s research is driven by a passion for improving healthcare delivery through the innovative application of artificial intelligence techniques. By leveraging her expertise in natural language processing and her deep understanding of clinical medicine, her doctoral work focuses on harnessing the power of cutting-edge AI technologies, such as large language models, to make health information more accessible, understandable, and actionable for patients and the general public.

PAPERS & PRESENTATIONS
Thomas Payne gave an invited plenary presentation titled “Artificial intelligence/machine learning in clinical care” at the Medicaid Medical Directors Network Spring Workshop in Seattle on May 2nd.

Presentations at EPIC XGM, Verona, WI:

  • Hasan Ahmad and Angad Singh presented “Two Thumbs Up: Streamlining User Feedback on Order Sets and SmartSets.”
  • Terri Kim and Angad Singh presented “Expanding Digital Access for Pregnancy Management Options.”
  • Angad Singh presented “The Ins and Outs of Advance Beneficiary Notices (ABNs)” and “Honoring Patient Identities Across the Care Continuum.”

UPCOMING EXAMS
General Exam
Title: Imaging-based breast cancer risk prediction
Student: Wesley Surento
Date/Time: Monday, May 13, 2024, 2 pm
Location: 1144 Eastlake Ave E, LG502
Zoom: https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09

Abstract: Many current breast cancer risk assessment tools applied in clinical settings utilize risk factors such as age, menopausal status, personal biopsy history, and family history of breast cancer, which reflect risk characteristics well at an aggregate level. While imaging markers could potentially better provide risk estimates at the individual level, they are not built into most breast cancer risk prediction models. There remains much unexplored potential in leveraging imaging markers as part of risk assessment models for prevention and early prediction.

In this work, we aim to develop and assess a new risk prediction model that incorporates MRI-derived features along with clinical risk factors. For Aim 1, we will build a collection of breast imaging and clinical risk factors for a population of women who underwent breast cancer screening. In addition to our repository of MR image data for the study cohort, a REDcap database will be used to store their clinical risk factors obtained from electronic health records, such as select demographic information, menopausal status, BRCA mutation, and family history of breast cancer. In Aim 2, we will develop image processing pipelines to extract quantitative breast imaging markers such as background parenchymal enhancement and apparent diffusion coefficient. We are interested in assessing their ability to predict breast cancer diagnosis in women at high risk. In Aim 3, we will develop a new breast cancer risk model that incorporates both imaging markers as well as clinical risk factors in a multimodal approach, and compare its performance against a conventional risk prediction tool.

ANNOUNCEMENTS

New $12.6M NIH grant awarded to UW Medicine researchers

Researchers from UW Psychiatry and Biomedical Informatics have received a 5-year grant to create a digital evaluation tool for people who experience hallucinations. Project staff include BIME constituents Meliha Yetisgen, Patrick Wedgeworth and Oliver Bear Don’t Walk, as well as two BIME PhD students (Weizhe Xu and Feng Chen) as RAs. Please see more information here.

Renewal of NIH Center for Reproducible Biomedical Modeling Grant

The Department of Bioengineering and Herbert M Sauro, PI, are pleased to announce that nearly $6 million will be provided by the National Institute of Biomedical Imaging and Bioengineering to continue the work of the Center. This is a unique collaborative, multi-institutional project, with leading investigators at the University of Connecticut (Ion Moraru, Eran Agmon), University of Wisconsin-Madison (Elebeoba May), the University of Auckland (David Nickerson) and the UW Department of Biomedical Informatics and Medical Education (John Gennari), as well as the UW eScience Institute (Joseph Hellerstein). The goal of the center is to improve multi-scale and integrative modeling of biomedical systems that can support precision medicine and credible predictive models for biomedicine. The center aims to CURE these models by making them credible, understandable, reproducible, and extensible.

Yue Guo will be joining UIUC iSchool as a tenure-track Assistant Professor this Fall.

April 29 – May 3, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Zhiyong Lu, PhD FACMI, FIAHSI
Thursday, May 9th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will be in-person

Title: Transforming Medicine with AI: from PubMed Search to TrialGPT
Abstract: The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. As such, the field of medicine is undergoing a paradigm shift driven by AI-powered analytical solutions. This talk explores the benefits (and risks) of AI and ChatGPT, highlighting their pivotal roles in revolutionizing biomedical discovery, patient care, diagnosis, treatment, and medical research. By demonstrating their uses in some real-world applications such as improving PubMed searches (Fiorini et al., Nature Biotechnology 2018), supporting precision medicine (LitVar, Allot et al., Nature Genetics 2023), and accelerating patient trial matching (TrialGPT), we underscore the potential of AI and ChatGPT in enhancing clinical decision-making, personalizing patient experiences, and accelerating knowledge discovery.

Speaker Bio: Dr. Zhiyong Lu is a tenured Senior Investigator at the NIH/NLM IPR, leading research in biomedical text and image processing, information retrieval, and AI/machine learning. In his role as Deputy Director for Literature Search at NCBI, Dr. Lu oversees the overall R&D efforts to improve literature search and information access in resources like PubMed and LitCovid, which are used by millions worldwide each day. Additionally, Dr. Lu is Adjunct Professor of Computer Science at the University of Illinois Urbana-Champaign (UIUC). Dr. Lu serves as an Associate Editor of Bioinformatics, Organizer of the BioCreative NLP challenge, and Chair of the ISCB Text Mining COSI. With over 350 peer-reviewed publications, Dr. Lu is a highly cited author, and a Fellow of the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

PAPERS & PRESENTATIONS
Bradley ER*, Portanova J*, Woolley JD, Buck B, Painter IS, Hankin M, Xu W, Cohen T. Quantifying abnormal emotion processing: a novel computational assessment method and application in schizophrenia. Psychiatry Research. 2024 Apr 4:115893. * denotes co-first authorship. (link)

UPCOMING EXAMS
Final Exam
Title: Transformative Diagnostics: Applying Transformer Networks and Semantic Guidance to Whole Slide Images
Student: Meredith (Wenjun) Wu
Date/Time: Monday, May 6 at 11 am
Location: Allen Center 303 Conference Room
Zoom: https://washington.zoom.us/j/97904765629

Abstract: This dissertation advances digital pathology by developing deep learning techniques for more accurate and efficient analysis of skin and breast cancer from whole slide images. It introduces innovative approaches like VSGD-Net and a two-stage segmentation method, along with transformer-based models such as HatNet and ScatNet, which leverage self-attention to understand contextual relationships within the images. A key innovation is the Semantics-Aware Attention Guidance framework that enhances diagnostic precision and interpretability by focusing on critical areas, significantly outperforming existing models. These advancements provide pathologists with powerful tools, bridging the gap between computational models and clinical applications, thereby improving early detection, diagnosis, and treatment of cancer.

General Exam
Title: Imaging-based breast cancer risk prediction
Student: Wesley Surento
Date/Time: Monday, May 13, 2024, 2 pm
Location: 1144 Eastlake Ave E, LG502
Zoom: https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09

Abstract: Many current breast cancer risk assessment tools applied in clinical settings utilize risk factors such as age, menopausal status, personal biopsy history, and family history of breast cancer, which reflect risk characteristics well at an aggregate level. While imaging markers could potentially better provide risk estimates at the individual level, they are not built into most breast cancer risk prediction models. There remains much unexplored potential in leveraging imaging markers as part of risk assessment models for prevention and early prediction.

In this work, we aim to develop and assess a new risk prediction model that incorporates MRI-derived features along with clinical risk factors. For Aim 1, we will build a collection of breast imaging and clinical risk factors for a population of women who underwent breast cancer screening. In addition to our repository of MR image data for the study cohort, a REDcap database will be used to store their clinical risk factors obtained from electronic health records, such as select demographic information, menopausal status, BRCA mutation, and family history of breast cancer. In Aim 2, we will develop image processing pipelines to extract quantitative breast imaging markers such as background parenchymal enhancement and apparent diffusion coefficient. We are interested in assessing their ability to predict breast cancer diagnosis in women at high risk. In Aim 3, we will develop a new breast cancer risk model that incorporates both imaging markers as well as clinical risk factors in a multimodal approach, and compare its performance against a conventional risk prediction tool.

ANNOUNCEMENTS
Zoljargal Lkhagvajav (Zoey) was an organizer of the workshop presented by the Mongolian Ministry of Health and the Ministry of Digital Development – “State Productivity Recovery – Digital Health Convergence Workshop” in April with the support of the Mongolian Prime Minister, UNICEF, and the Asia eHealth Information Network. Almost 300 participants from various sectors gathered to attend the discussion about Mongolia’s current digital health landscape and the development of the National Digital Health Strategy. More details are available here.

Janice Sabin was invited by the National Institutes of Health and the National Institute on Minority Health and Health Disparities (NIH/NIMHD) to participate as a discussant in an upcoming workshop titled, Addressing the Influence of Interpersonal Biases on Health Outcomes and Disparities, June 18, 2024. Knowledge from the workshop will help NIH assess the need for future funding opportunities in this understudied area. Participants will include federal program staff, and research scientists and clinicians with expertise in the science of interpersonal biases (implicit and explicit stereotyping and prejudice) and health.

April 22 – April 26, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Casey Overby Taylor, Ph.D. (she/her)
Thursday, May 2nd – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Detecting and Mitigating Telehealth-Generated Inequities
Abstract: Although telehealth holds great promise to provide better access to care and improved clinical outcomes, for some groups, the benefits may not be fully realized. Here we will discuss work studying differences in patterns of telehealth adoption in terms of health equity determinants. We will also describe lessons learned from our work piloting an intervention to improve access to video visits for underserved patient groups.

Speaker Bio: Casey Overby Taylor is Associate Professor of Medicine – General Internal Medicine (GIM) and Biomedical Engineering in the Johns Hopkins (JH) School of Medicine, Associate Director of the JH Institute for Computational Medicine, and member of the JH Malone Center for Engineering in Healthcare. She is affiliated with the GIM Biomedical Informatics Data Science Section, and has joint appointments in the Dept. of Health Policy and Management in the Johns Hopkins Bloomberg School of Public Health, and the Computer Science Dept. in the Johns Hopkins Whiting School of Engineering. Her research draws from biomedical informatics and the related field of biomedical data science, to address the challenge of how to incorporate digital health technologies into clinical practice. The mission of her research group, Translational Informatics Research and Innovation (TIRI) Lab, is to understand and create advanced technology and digital device solutions that address challenges to the translation of biomedical data science-informed guidance into clinical use to improve the health of individuals, especially for people that are often underrepresented in research.

UPCOMING EXAMS
Title: Assessing Disparities Through Missing Race and Ethnicity Data: Results from a Juvenile Arthritis Registry
Student: Katelyn Banschbach
Date/Time: Monday 4/29 at 2:30-3:50
Location: Zoom only – https://washington.zoom.us/my/peter.th

 Abstract: Racial and ethnic minorities remain underrepresented in research despite similar willingness to participate.  Incomplete race and ethnicity data can lead to exclusion in analysis and those missing this data are more likely to be Black or Hispanic, further worsening disparities.  Research and secondary analytics done with incomplete race and ethnicity can unintentionally worsen disparities.  Alternatively, missing data may obscure disparities which are already present.  Ensuring high quality race and ethnicity data within the EHR and across linked systems, such as patient registries, allows identification of disparities and is necessary to achieve a goal of inclusion of racial and ethnic minorities in scientific research.

Missing race and ethnicity data was assessed and completed within Pediatric Rheumatology Care Outcomes Improvement Network (PR-COIN). The project consists of 4 Aims: (1) Identifying baseline missing race and ethnicity data, (2) Understand current race and ethnicity collection practices and entry into the registry at each center via a REDCap survey, (3) Data completion through three audit and feedback cycles where reports of patients with missing data are sent to each center with request for manual completion via EHR data, (4) Impact assessment on outcome measures via comparison of racial and ethnic differences in risk of certain outcome measures such as elevated clinical juvenile arthritis disease activity score (cJADAS) which are compared pre and post data completion.

The PR-COIN database contains over 5,000 active patients with juvenile idiopathic arthritis spanning 50,000 encounters with plans to add more pediatric rheumatologic diseases over time. Completing missing race and ethnicity data will help avoid unintentionally building inequitable algorithms and system frameworks. We describe the process of identifying and completing missing race and ethnicity data at six centers within the PR-COIN network and highlight the impact of completed data on outcome assessments.

April 15 – April 19, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Brody H Foy, DPhil
Thursday, April 25th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Mathematical and computational frameworks for adaptively benchmarking patients
Abstract: When a patient has bloodwork analyzed, results are typically benchmarked against crude, population-level definitions of normality, without consideration of what ‘healthy’ means for the given patient, in their given context. In this talk, I will overview various computational approaches we have developed to construct adaptive, and patient-specific definitions of ‘normality’. I will illustrate how these approaches can help improve diagnostic and prognostic evaluation, while also leading to a variety of novel mechanistic insights into human physiology.

Speaker Bio: Dr. Foy is a mathematician whose research is focused on improving usage of routine clinical data sources. His lab develops computational tools for high-throughput analysis of laboratory test results and associated raw data streams, with particular emphasis on hematology, and aims to build deployable tools, which are useful in both high- and low-resource settings. He did his DPhil in computer science at the University of Oxford, as a Rhodes Scholar, and undertook postdoctoral training in systems biology at Harvard Medical School.

PAPERS & PRESENTATIONS
Yeon Mi Hwang, Qi Wei, Samantha Piekos, Bhargav Vemuri,Sevda Molani, Philip Mease, Leroy Hood, Jennifer Hadlock. Maternal-fetal outcomes in patients with immune-mediated inflammatory diseases, with consideration of comorbidities: a retrospective cohort study in a large U.S. healthcare system. Lancet eClinicalMedicine. Feb 1, 2024. https://doi.org/10.1016/j.eclinm.2024.102435

ANNOUNCEMENTS
Oliver Bear Don’t Walk IV has received a K99/R00 grant from NLM titled “Collaboratively Identifying Population-Specific Social Determinants of Health for Indigenous Patients Living with HIV: From Patient Perspectives to the Electronic Health Record.” Andrea Hartzler, Meliha Yetisgen, Heidi Crane, Vanessa Simonds, Jason Deen, and Peter Tarczy-Hornoch will provide mentorship. The project aims to will connect Indigenous knowledge on social determinants of health (SDH) with the EHR.

April 8 – April 12, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Taryn Hall, MPH PhD
Thursday, April 18th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Understanding payer coverage requirements for novel health technologies
Abstract: Reimbursement is a crucial step in translating medical advances from bench to bedside. In this talk we will discuss how payers view novel health technologies and the evidence required for payer coverage. Additionally, we will cover common missteps innovators make as they attempt to bring new discoveries to market. 
Speaker Bio: Dr. Taryn Hall is a Senior Director at Optum Genomics, a precision medicine team within Unitedhealth Group (UHG) — a large healthcare company that includes a payer and healthcare services. As a subject matter expert, Dr. Hall provides precision medicine strategy consulting and innovative health program and product design for stakeholders throughout the UHG enterprise as well as biotechnology industry clients. She holds a PhD in Public Health Genetics from the University of Washington School of Public Health and was a National Library of Medicine Fellow in Biomedical and Health Informatics in the University of Washington BIME department.

PAPERS & PRESENTATIONS
Zhaoyi Sun, Yujuan Fu, Wen-Wai Yim, Meliha Yetisgen and Fei Xia. An Enhanced Multimodal Multilingual Dataset for Medical Misinformation Detection. Accepted by the 12th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024).

UPCOMING EXAM
Title: Assessing Disparities Through Missing Race and Ethnicity Data: Results from a Juvenile Arthritis Registry
Student: Katelyn Banschbach
Date/Time: Monday 4/29 at 2:30-3:50
Location: Zoom only – https://washington.zoom.us/my/peter.th

Abstract: Racial and ethnic minorities remain underrepresented in research despite similar willingness to participate.  Incomplete race and ethnicity data can lead to exclusion in analysis and those missing this data are more likely to be Black or Hispanic, further worsening disparities.  Research and secondary analytics done with incomplete race and ethnicity can unintentionally worsen disparities.  Alternatively, missing data may obscure disparities which are already present.  Ensuring high quality race and ethnicity data within the EHR and across linked systems, such as patient registries, allows identification of disparities and is necessary to achieve a goal of inclusion of racial and ethnic minorities in scientific research.
Missing race and ethnicity data was assessed and completed within Pediatric Rheumatology Care Outcomes Improvement Network (PR-COIN). The project consists of 4 Aims: (1) Identifying baseline missing race and ethnicity data, (2) Understand current race and ethnicity collection practices and entry into the registry at each center via a REDCap survey, (3) Data completion through three audit and feedback cycles where reports of patients with missing data are sent to each center with request for manual completion via EHR data, (4) Impact assessment on outcome measures via comparison of racial and ethnic differences in risk of certain outcome measures such as elevated clinical juvenile arthritis disease activity score (cJADAS) which are compared pre and post data completion.
The PR-COIN database contains over 5,000 active patients with juvenile idiopathic arthritis spanning 50,000 encounters with plans to add more pediatric rheumatologic diseases over time. Completing missing race and ethnicity data will help avoid unintentionally building inequitable algorithms and system frameworks. We describe the process of identifying and completing missing race and ethnicity data at six centers within the PR-COIN network and highlight the impact of completed data on outcome assessments.

April 1 – April 5, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Aakash Sur, PhD
Thursday, April 11th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Bioinformatics in Oncology Drug Discovery

Abstract: In the ever-evolving landscape of oncology drug development, biologics have surged in popularity in recent years, and have been adopted as frontline therapy for several indications. Antibody drug conjugates offer a targeted approach to cancer treatment, delivering payloads specifically to cancerous cells and stimulating immune cells instead of relying on the systemic effects of a small molecule. This talk will explore the applications of bioinformatics in early discovery, where potential targets are validated using extensive multi-omic datasets, sequences are confirmed using transcriptome assembly, and antibodies are sequenced with long-read technology. Additionally, as promising payloads and targets are identified, single cell experiments can help elucidate their mechanism of action and understand how the tumor and tumor microenvironment change as a response to therapeutics.

Speaker Bio: Aakash Sur earned a B.S. in Biochemistry and a B.A. in humanities from the University of Texas in 2015 and received a PhD in Biomedical Informatics from the University of Washington in 2022. His thesis work focused on leveraging machine learning to improve the genome assembly of newly sequenced species. Since then, he joined Seagen as a scientist in their research organization with a purview in sequencing related experiments and a focus on single cell technologies. He continues his journey at Pfizer after their acquisition of Seagen in 2023.

PAPERS & PRESENTATIONS
Faisal Yaseen, Rafael Santana-Davila, Clemens Grassberger, Delphine L. Chen, Christina Baik, Keith D. Eaton, Diane Tseng, Smitha Patiyil Menon, Lei Deng, Sylvia Lee, Ariana D Jimenez, Paul D Lampe, A. McGarry Houghton, Paul E Kinahan, Jing Zeng, Stephen R. Bowen. Early FDG PET imaging and circulating T-cell repertoire biomarkers of response to chemotherapy and PD-1 checkpoint inhibitors in patients with stage IV NSCLC. 2024 ASCO Annual Meeting.

ANNOUNCEMENTS
A Population Health Initiative Tier 2 Pilot Grant titled “Culturally adapting and pilot testing chatbot-delivered psychotherapy for Chinese American families caring for older adults with chronic conditions” was funded. The PI is Jingyi Li, and the Co-Investigators are Serena Jinchen Xie, Weichao Yuwen, & Trevor Cohen.

March 25 – March 29, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Laura Wiley, PhD
Thursday, April 4th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: The Importance of Computational Phenotyping in a Learning Healthcare System

Abstract: Every step within the learning healthcare system is dependent upon accurate identification of the patient population of interest. Just as inclusion/exclusion criteria impact the generalizability of randomized controlled trials, the algorithm used for population identification impacts the applicability and generalizability of EHR-based evidence generation. This seminar will discuss my group’s effort to improve the equity and reproducibility of this process to ensure that all patients receive equal benefit from the learning health system.

Speaker Bio:Dr. Laura Wiley is an Associate Professor of Biomedical Informatics at the University of Colorado Anschutz Medical Campus. Her work focuses on computational phenotyping and other informatics methodologies for clinical evidence generation in support of precision medicine. She is an active educator directing the Coursera Clinical Data Science Specialization – a series of 6 MOOCs providing hands on training in clinical research informatics. She is also a national leader in the American Medical Informatics Association having served as Chair or Vice Chair of several AMIA conferences.

PAPERS & PRESENTATIONS
Feng Chen, Liqin Wang, Julie Hong, Jiaqi Jiang, Li Zhou. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. Journal of American Medical Informatics Association. 23 March, 2024. https://doi.org/10.1093/jamia/ocae060.

Siru Liu, Allison B. McCoy, Aileen P. Wright, Scott D. Nelson, Sean S. Huang, Hasan B. Ahmad, Sabrina E. Carro, Jacob Franklin, James Brogan, Adam Wright.  Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support. Journal of the American Medical Informatics Association, 2024, 1–9 https://doi.org/10.1093/jamia/ocae041.

F.L. Nkoy, B.L. Stone, Y. Zhang, and G. Luo. A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection. JMIR Medical Informatics, 2024.

ANNOUNCEMENT
Bhargav Vemuri was selected for the ITHS TL1 Translational Research Training Program’s 2024-2025 cohort for his research proposal on using deep learning to reveal subgroups of developmental trajectories in adolescents. TL1 is a one-year mentored research training program in translational science for predoctoral students.

March 18 – March 22, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Jennifer Hadlock, MD
Thursday, March 28th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: Accelerating research for disease prevention: risk-enriched cohorts for observational studies
Abstract:To accelerate research into disease prevention, it is valuable to conduct prospective studies of the trajectories that begin before disease onset. However, the large cohort size needed can be a deterrent for using advanced methods of observation. This can drive large national studies to use narrow sets of well-established observation modalities. For newer modalities, the cost of large cohorts is a deterrent that drives research toward more reactive, post-diagnosis investigations. Risk-enrichment provides an opportunity to reduce the size of the cohort needed for pre-onset studies of disease trajectories. However, whether using traditional risk scores or new machine learning models, this approach comes with both explicit and potentially hidden trade-offs. Here, we discuss a formal approach for risk-enrichment to reduce cohort size for studies of new-onset diagnosis, and propose approaches for assessing and addressing trade-offs.

Speaker Bio: Dr. Jennifer Hadlock’s research focuses on accelerating translational research into transitions between wellness and disease, by integrating clinical data into systems biology at scale. Her lab develops models from high-fidelity, longitudinal observations of multiomics, phenotype, exposures and patient-reported outcomes. Specific areas of focus are immune-mediated inflammatory disease, maternal-fetal health, and chronic multimorbidity. She is also a PI on the NIH NCATS Biomedical Translator Consortium. She received her MD at the University of Washington School of Medicine, including training in the Rural/Urban Underserved Pathway. Prior to that, she was a Principal Software Engineer in research and development at Microsoft, analyzing and optimizing end-user quality for natural language processing, geographic information systems and real-time digital imaging. She successfully led numerous teams working on technologies used by hundreds of millions of people worldwide.

PAPERS & PRESENTATIONS
Yue Guo, Joseph Chee Chang, Maria Antoniak, Erin Bransom, Trevor Cohen, Lucy Lu Wang, Tal August. Personalized Jargon Identification for Enhanced Interdisciplinary Communication. Accepted by NAACL 2024.

ANNOUNCEMENTS
Ehsan Alipour was selected as a recipient of this year’s Reviewer Award for the AMIA Informatics Summit. Nice work!

Anne Turner will be presenting a webinar entitled “Decision-Making in Dementia Care: Preferences of People with Memory Loss” at the PennAITech Collaboratory for Healthy Aging Webinar Series on April 4th. To register, please click here.

March 11 – March 15, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590 – On break until March 28th!

PAPERS & PRESENTATIONS
Yujuan Fu*, Giridhar Kaushik Ramachandran*, Nicholas J Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia,  ̈Özlem Uzuner, and Meliha Yetisgen. Extracting social determinants of health from pediatric patient notes using large language models: Novel corpus and methods. Accepted by LREC-COLING, 2024.

Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, and Meliha Yetisgen. To err is human, how about medical large language models? comparing pre-trained language models for medical assessment errors and reliability. Accepted by LREC-COLING, 2024

Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Özlem Uzuner, Martin Gunn and Meliha Yetisgen.  A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models. Accepted for 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).