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News and Events

 

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

June 9, 2025 – June 13, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/25/2025!

ANNOUNCEMENTS
You’re invited to help us celebrate 2024-25 BHI graduates, new emeritus faculty, and student/faculty awardees at the BHI End-of-Year Graduation Celebration! Here are the details:

When: Ceremony this Friday, June 13th from 2:00-3:00pm with reception immediately following from 3:00-5:00pm

Where:

  • SLU Orin Smith Auditorium for the 2:00-3:00pm ceremony or remotely (Zoom link below);
  • SLU 123 A & B for the 3:00-5:00pm reception (in person only)

Zoom link: https://washington.zoom.us/j/95887407122?pwd=8BbgfuyqBR2ISMEuthvMUV9P88qU5J.1

_______________________
Authors: Jimmy Phuong, MSPH, PhD; Adam B Wilcox, PhD, FAMIA; Shruti Sehgal, MD(Hom), MS; Anthony Solomonides, PhD, MSc, FAMIA, FACMI; Juan Espinoza, MD

Title: Maturity Models 101 – What does Roadmapping Institutional Growth in Informatics look like?. [Tutorial session]
Date: 11/15/2025
Time: 8:30 AM – 12:00 PM EST
Session Code: W04

_______________________
Please join us in congratulating Ehsan Alipour who successfully passed his PhD Defense!
Title: Evaluating Multi-Modal Data Fusion Approaches for Predictive Clinical Models Using Multiple Medical Data Domains

Abstract: Multimodal deep learning models have emerged as powerful tools in biomedical research, offering the ability to integrate diverse data sources such as clinical records, multi-omics data, imaging, survey responses, and wearable data to enhance predictive accuracy and deepen understanding of complex medical phenomena. Central to multimodal modeling is the process of data fusion, where information from different modalities is integrated in a unified model. Three primary fusion strategies exist in deep learning: early fusion (feature-level), intermediate fusion and late fusion (decision-level). While widely adopted in other domains, their comparative performance and implementation considerations remain underexplored in biomedical applications, where data heterogeneity, missingness, and varying dimensionality present additional challenges.

This dissertation aims to evaluate the implications of data fusion strategies in developing multimodal predictive models in medicine. Across three distinct aims, we assess the impact of early, intermediate, and late fusion techniques on predictive performance, implementation complexity, and generalizability using diverse combinations of data types, outcomes, and modeling strategies. These studies span multiple datasets and outcome types (binary vs continuous) providing a broad view of fusion strategy utility in real-world biomedical settings.

In Aim 1, we evaluated and compared early, intermediate, and late fusion strategies for integrating longitudinal EHR, genomic, and survey data to predict chronic kidney disease (CKD) progression in patients with type 2 diabetes using a novel transformer-based multimodal architecture. Using data from the NIH’s All of Us initiative, we trained models on a cohort of approximately 40,000 patients. While unimodal models—particularly those based on EHR data—achieved strong baseline performance with an AUROC of 0.73 (0.71 – 0.75), the inclusion of multimodal data offered only marginal improvement with an AUROC of 0.74 (0.72 – 0.76), with the benefit limited to the early fusion approach and lacking statistical significance. This aim highlighted the challenges of overfitting in complex fusion architectures and emphasized the role of modality-specific predictive strength.

In Aim 2, we extended the fusion analysis to imaging data by combining a convolutional neural network (CNN) trained on longitudinal cross-sectional imaging with a shallow neural network trained on clinical and pathology variables to predict post-surgical margin status in patients with soft tissue sarcoma (n=202). Here, the intermediate fusion strategy significantly outperformed other approaches, achieving an AUROC of 0.80 (0.66–0.95), suggesting that cross-modal interactions between histologic features and imaging embeddings may be best captured through intermediate fusion. This result demonstrated the potential value of intermediate fusion when complementary signals exist across modalities.

In Aim 3, we explored fusion strategies for estimating continuous CT-derived body composition metrics (e.g., visceral, and subcutaneous fat volumes) using only chest radiographs and clinical variables in a dataset of 1,088 patients. A multitask multimodal model was developed and evaluated across early, intermediate, and late fusion strategies. Late fusion consistently delivered the best performance across most body composition metrics, closely followed by intermediate fusion. These results suggest that when individual modalities offer high independent predictive power, decision-level integration may be optimal for regression tasks.

Collectively, this work provides a comprehensive evaluation of data fusion strategies in multimodal biomedical modeling, highlighting their strengths, limitations, and practical considerations. Findings suggest that no single fusion strategy universally outperforms the others; rather, optimal fusion depends on data characteristics, model architecture, and task-specific objectives. This dissertation lays the groundwork for future research aimed at developing adaptive fusion strategies tailored to the complexities of real-world biomedical data.

PAPERS & PUBLICATIONS

 

June 2, 2025 – June 6, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/25/2025!

ANNOUNCEMENTS
Andrea Hartzler was elected serve on the SOM Council on Appointments and Promotions for a 3-year term!

_______________________

Please join us in congratulating Velvin Fu who successfully passed her PhD Defense!

Title: Evaluating and Enhancing Large Language Models (LLMs) in the Clinical Domain

Abstract: Recent advancements in large language models (LLMs) have demonstrated human-level performance on many specialized medical tasks, even without annotated training data. However, three main challenges remain: (1) due to the sensitive and highly specialized nature of clinical narratives, as well as the high cost of human expert annotation, there is a lack of high-quality, well-structured, and clinically meaningful datasets for LLM training and evaluation; (2) current medical LLMs show limited generalization ability to interpret and extract complex clinical information on certain unseen natural language understanding (NLU) tasks; and (3) as LLMs are typically trained on vast amounts of data, there is a substantial risk of data contamination, where evaluation benchmarks unintentionally overlap with training data, leading to inflated test performance and potentially reduced performance on truly novel tasks.

In this work, we address these limitations through three core aims: (1) develop benchmark datasets for clinical information extraction (IE), a key NLU subtask, across two critical medical domains, and evaluate the performance of multiple state-of-the-art (SOTA) transformer-based language models (LMs), under both fine-tuning and in-context learning settings; (2) develop a more generalizable medical NLU model via instruction tuning, demonstrating enhanced performance on previously unseen clinical NLU datasets; and (3) systematically review existing detection approaches for data contamination and evaluate those approaches on datasets used during pre-training and fine-tuning LLMs, with our own and three other widely used open-source LLMs.

In summary, our work contributes to the development of both clinical benchmarks and robust LLMs, as well as highlighting the ongoing challenges in benchmarking LLMs’ generalizability.

UPCOMING EXAMS
Title: Evaluating Multi-Modal Data Fusion Approaches for Predictive Clinical Models Using Multiple Medical Data Domains
Student: Ehsan Alipour
Date/Time: Wednesday, June 11, 2025, 11am – 1 pm PT
In-person location: 850 Republican Street, Building C, SLU C259
Zoom: https://washington.zoom.us/my/peter.th

Abstract: Multimodal deep learning models have emerged as powerful tools in biomedical research, offering the ability to integrate diverse data sources such as clinical records, multi-omics data, imaging, survey responses, and wearable data to enhance predictive accuracy and deepen understanding of complex medical phenomena. Central to multimodal modeling is the process of data fusion, where information from different modalities is integrated in a unified model. Three primary fusion strategies exist in deep learning: early fusion (feature-level), intermediate fusion and late fusion (decision-level). While widely adopted in other domains, their comparative performance and implementation considerations remain underexplored in biomedical applications, where data heterogeneity, missingness, and varying dimensionality present additional challenges.

This dissertation aims to evaluate the implications of data fusion strategies in developing multimodal predictive models in medicine. Across three distinct aims, we assess the impact of early, intermediate, and late fusion techniques on predictive performance, implementation complexity, and generalizability using diverse combinations of data types, outcomes, and modeling strategies. These studies span multiple datasets and outcome types (binary vs continuous) providing a broad view of fusion strategy utility in real-world biomedical settings.

In Aim 1, we evaluated and compared early, intermediate, and late fusion strategies for integrating longitudinal EHR, genomic, and survey data to predict chronic kidney disease (CKD) progression in patients with type 2 diabetes using a novel transformer-based multimodal architecture. Using data from the NIH’s All of Us initiative, we trained models on a cohort of approximately 40,000 patients. While unimodal models—particularly those based on EHR data—achieved strong baseline performance with an AUROC of 0.73 (0.71 – 0.75), the inclusion of multimodal data offered only marginal improvement with an AUROC of 0.74 (0.72 – 0.76), with the benefit limited to the early fusion approach and lacking statistical significance. This aim highlighted the challenges of overfitting in complex fusion architectures and emphasized the role of modality-specific predictive strength.

In Aim 2, we extended the fusion analysis to imaging data by combining a convolutional neural network (CNN) trained on longitudinal cross-sectional imaging with a shallow neural network trained on clinical and pathology variables to predict post-surgical margin status in patients with soft tissue sarcoma (n=202). Here, the intermediate fusion strategy significantly outperformed other approaches, achieving an AUROC of 0.80 (0.66–0.95), suggesting that cross-modal interactions between histologic features and imaging embeddings may be best captured through intermediate fusion. This result demonstrated the potential value of intermediate fusion when complementary signals exist across modalities.

In Aim 3, we explored fusion strategies for estimating continuous CT-derived body composition metrics (e.g., visceral, and subcutaneous fat volumes) using only chest radiographs and clinical variables in a dataset of 1,088 patients. A multitask multimodal model was developed and evaluated across early, intermediate, and late fusion strategies. Late fusion consistently delivered the best performance across most body composition metrics, closely followed by intermediate fusion. These results suggest that when individual modalities offer high independent predictive power, decision-level integration may be optimal for regression tasks.

Collectively, this work provides a comprehensive evaluation of data fusion strategies in multimodal biomedical modeling, highlighting their strengths, limitations, and practical considerations. Findings suggest that no single fusion strategy universally outperforms the others; rather, optimal fusion depends on data characteristics, model architecture, and task-specific objectives. This dissertation lays the groundwork for future research aimed at developing adaptive fusion strategies tailored to the complexities of real-world biomedical data.

 

May 26, 2025 – May 30, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Kari Stephens, PhD
Thursday, June 5th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present via Zoom

Title:
Digital Behavioral Health Therapeutics: Is there an app for that and can AI be your next therapist?

Abstract:
Primary care treats the majority of mental and behavioral health conditions that are also on the rise, due to large care access issues. Integrating behavioral health providers into primary care is aiming to meet that need, but practices struggle with dissemination. Digital therapeutics may hold great promise to meet the growing need.

Speaker Bio:
Dr. Kari Stephens is Vice Chair of Research, Helen D. Cohen Endowed Professor, Director of Clinical Research Informatics and Professor in Family Medicine and Adjunct Professor in Biomedical Informatics and Medical Education at the UW School of Medicine. Dr. Stephens is a practicing clinical psychologist and biomedical informaticist conducting research focused on health equity, integrated behavioral health, chronic pain, posttraumatic stress disorder, anxiety, depression, substance use, cancer, long COVID, and informatics data sharing, particularly in primary care settings. Dr. Stephens currently conducts clinical research and leads informatics and innovations in data sharing as Director of Data QUEST, overseeing a regional electronic health record systems based primary care data sharing network, which has supported over $100M in grant funded projects, Associate Director with the National Alzheimer’s Coordinating Center, and executive faculty member and lead of regional informatics within the Institute of Translational Health Science’s Biomedical Informatics Core. Her work has been funded by NIH, PCORI, AHRQ, SAHMSA, DoD, CDC, private agencies, and local and state government agencies.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

UPCOMING EXAMS
Title: Evaluating and Enhancing Large Language Models (LLMs) in the Clinical Domain
Student: Velvin Fu
Date/Time: Thursday, June 5, 2025,  11am – 1 pm PT
In-person location: 850 Republican Street, Building C, SLU C259
Zoom: https://washington.zoom.us/my/melihay

Abstract: Recent advancements in large language models (LLMs) have demonstrated human-level performance on many specialized medical tasks, even without annotated training data. However, three main challenges remain: (1) due to the sensitive and highly specialized nature of clinical narratives, as well as the high cost of human expert annotation, there is a lack of high-quality, well-structured, and clinically meaningful datasets for LLM training and evaluation; (2) current medical LLMs show limited generalization ability to interpret and extract complex clinical information on certain unseen natural language understanding (NLU) tasks; and (3) as LLMs are typically trained on vast amounts of data, there is a substantial risk of data contamination, where evaluation benchmarks unintentionally overlap with training data, leading to inflated test performance and potentially reduced performance on truly novel tasks.

In this work, we address these limitations through three core aims: (1) develop benchmark datasets for clinical information extraction (IE), a key NLU subtask, across two critical medical domains, and evaluate the performance of multiple state-of-the-art (SOTA) transformer-based language models (LMs), under both fine-tuning and in-context learning settings; (2) develop a more generalizable medical NLU model via instruction tuning, demonstrating enhanced performance on previously unseen clinical NLU datasets; and (3) systematically review existing detection approaches for data contamination and evaluate those approaches on datasets used during pre-training and fine-tuning LLMs, with our own and three other widely used open-source LLMs.

In summary, our work contributes to the development of both clinical benchmarks and robust LLMs, as well as highlighting the ongoing challenges in benchmarking LLMs’ generalizability.

Title: Evaluating Multi-Modal Data Fusion Approaches for Predictive Clinical Models Using Multiple Medical Data Domains
Student: Ehsan Alipour
Date/Time: Wednesday, June 11, 2025, 11am – 1 pm PT
In-person location: 850 Republican Street, Building C, SLU C259
Zoom: https://washington.zoom.us/my/peter.th

 

May 19, 2025 – May 22, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Lucy Lu Wang, PhD
Thursday, May 29th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present in-person 

Title:
Evaluation and Meta-Evaluation of LLMs for Scientific and Medical Applications

Abstract:
Large Language Models (LLMs) are powering new opportunities to improve access to scientific and medical knowledge—especially for patients, caregivers, and other non-expert audiences. But these models do not always produce reliable or trustworthy information, and evaluating their performance remains a major challenge. Prior evaluation methods may not generalize to complex, retrieval-augmented, long-form generation tasks like literature review generation or plain language summarization. In this talk, I will introduce some settings in which LLMs are being used to support understanding of biomedical research, and present several recent studies of how to evaluate models more effectively. I will also discuss meta-evaluation strategies (how to evaluate the evaluation methods themselves) that can be used to probe model performance in these complex settings, and how our insights can help guide the use of LLMs in real-world healthcare and scientific communication.

Speaker Bio:
Lucy Lu Wang is an Assistant Professor at the University of Washington Information School, where she leads the Language Accessibility Research (LARCH) lab. She is also an affiliated Research Scientist at the Allen Institute for AI (Ai2), and holds adjunct appointments in UW Computer Science & Engineering, Biomedical Informatics & Medical Education, and Human Centered Design & Engineering. Her work spans scholarly document understanding, scientific evidence synthesis, and health communication. Specifically, she focuses on language technologies to improve access to and understanding of information in high-expertise domains like science and healthcare, with an emphasis on dataset development and evaluation practices. She is a core contributor to open science initiatives including Semantic Scholar and the COVID-19 Open Research Dataset, and public-facing systems focused on accessibility of science such as supp.ai and Paper to HTML. She received her PhD in Biomedical Informatics & Medical Education from the University of Washington.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

ANNOUNCEMENTS
Please join us in congratulating Amber Chen who successfully passed her General Exam!

Title: Computational Approaches to Predict Health Outcomes Using Cytometry Data

Abstract: Flow cytometry plays a pivotal role in medicine, as demonstrated by the widespread use of the complete blood count (CBC) across medicine. Alongside summary markers such as cell counts, CBCs also generate rich single-cell datasets, which are drastically underused. To allow for enhanced clinical use of these single-cell data streams, there is a need for methods which can generate clinically relevant biomarkers, or which can directly use these data for clinical prediction. To tackle these issues, we propose three primary research aims: (1) algorithmically developing novel markers from clinical single-cell data using unsupervised techniques, (2) evaluate and develop novel single-cell data modeling methods for predicting health outcomes, and (3) applying these single-cell approaches to significant health conditions, including predicting pregnancy and cardiac surgery complications and predicting treatment efficacy for hematologic malignancies. Approaches will be validated using large-scale data archives from University of Washington Medical Center (UWMC), alongside multiple open-source single-cell datasets. Collectively, this project will lead to generation of novel computational methods which can create tangible benefit for multiple well-defined clinical challenges.

PAPERS & PUBLICATIONS

  • Jie Fu, Sharon Pai, Daniel Hippe, Faisal Yaseen, John Kang, Ramesh Rengan, Jing Zeng, Stephen Bowen, Sunan Cui. “Predicting Voxel-Level Local Progression for Locally Advanced NSCLC following Chemoradiation Using Multi-Task Deep Learning” has been accepted for presentation in ORAL scientific session for the 67th annual meeting of American Society for Radiation Oncology (ASTRO)
  • W Sami, F Abbas, M Sabir, A Razzaq, H Nazeer, MA Hayat, M Ali, MI Ali, F Yaseen, U Nazir “Data-Driven Prediction of Malaria Outbreaks in Nigeria” was accepted in IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

UPCOMING EXAMS
Title: Evaluating and Enhancing Large Language Models (LLMs) in the Clinical Domain
Student: Velvin Fu
Date/Time: Thursday, June 5, 2025,  11am – 1 pm PT
In-person location: 850 Republican Street, Building C, SLU C259
Zoom: https://washington.zoom.us/my/melihay

Abstract: Recent advancements in large language models (LLMs) have demonstrated human-level performance on many specialized medical tasks, even without annotated training data. However, three main challenges remain: (1) due to the sensitive and highly specialized nature of clinical narratives, as well as the high cost of human expert annotation, there is a lack of high-quality, well-structured, and clinically meaningful datasets for LLM training and evaluation; (2) current medical LLMs show limited generalization ability to interpret and extract complex clinical information on certain unseen natural language understanding (NLU) tasks; and (3) as LLMs are typically trained on vast amounts of data, there is a substantial risk of data contamination, where evaluation benchmarks unintentionally overlap with training data, leading to inflated test performance and potentially reduced performance on truly novel tasks.

In this work, we address these limitations through three core aims: (1) develop benchmark datasets for clinical information extraction (IE), a key NLU subtask, across two critical medical domains, and evaluate the performance of multiple state-of-the-art (SOTA) transformer-based language models (LMs), under both fine-tuning and in-context learning settings; (2) develop a more generalizable medical NLU model via instruction tuning, demonstrating enhanced performance on previously unseen clinical NLU datasets; and (3) systematically review existing detection approaches for data contamination and evaluate those approaches on datasets used during pre-training and fine-tuning LLMs, with our own and three other widely used open-source LLMs.

In summary, our work contributes to the development of both clinical benchmarks and robust LLMs, as well as highlighting the ongoing challenges in benchmarking LLMs’ generalizability.

 

May 12, 2025 – May 16, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Elizabeth Putnam, PhD
Thursday, May 22nd – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present Via Zoom

Title:
Joys and challenges of research with small, rural communities

Abstract:
The establishment of trust relationships has been critical to research progress with small rural and tribal communities and thus has become a central theme of community focused research. Understanding the contexts presented by these communities has educated investigators seeking to connect with them to address local concerns about research and potential resulting discrimination. The challenges of recruiting participants from small populations and difficulties in reaching valid conclusions in these studies will be addressed.

Speaker Bio:
Elizabeth Putnam is Professor of Molecular Genetics and Toxicology at the University of Montana, serving as Chair of the Department of Biomedical and Pharmaceutical Sciences in the Skaggs School of Pharmacy since 2015. After completing undergraduate work in Biochemistry at Rutgers College in New Brunswick, NJ, she earned a PhD in Biomedical Sciences from the University of Texas-Houston Graduate School of Biomedical Sciences. Following postdoctoral fellowships at M.D. Anderson Cancer Center and the University of Texas Medical School, she moved to the University of Montana in 2000. Her research efforts include outreach to rural and underserved communities including projects to provide comprehensive, collaborative and participatory initiatives that specifically increased community understanding of the interplay between genes and environment. Since 2007, this has included CBPR with the Confederated Salish and Kootenai Tribes to develop community understanding of precision medicine while learning from these communities of the appropriate, culturally respectful ways to disseminate information about precision medicine opportunities.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

ANNOUNCEMENTS
Chris Lewis was selected as a 2025 Innovators & Influencer Honoree for the American Academy of Physical Medicine & Rehabilitation (AAPM&R)!
https://www.aapmr.org/members-publications/member-recognition/innovatorsandinfluencers

_______________________
Have a new idea that you think will transform medicine? If so, be sure to review the details in our 2025 RFA and submit a proposal by May 23rd, 2025.

We are seeking applications for one-year pilot projects in the area of medical data science, for potentially up to $45K, with $25K guaranteed direct costs + the possibility of adding up to $20K in cloud credits, pending agreement with cloud service providers.

Please note that only UW faculty are eligible to apply. Learn more here.
_______________________
2025 UW Wellness Symposium
Enhance your well-being at the 2025 UW Wellness Symposium, Wednesday, May 21, and Thursday, May 22. Join expert-led virtual sessions on mental, physical and financial health with The Whole U.

PAPERS & PUBLICATIONS

  • Riley BC, Phuong J, Hasan RA, Stansbury LG, Hess JR, Roubik DJ. Cold-stored versus room-temperature platelets for hemostasis in combat trauma patients. Transfusion. (Accepted on 30 Apr 2025).
  • S. Zeng, N.C. Jani, A.M. Sotolongo, G. Luo, M. Arjomandi, and M.J. Falvo. Clinical Utility of Pulmonary Function Testing in Assessing Longitudinal Outcomes of Deployed Veterans with Preserved Spirometry. Annals of the American Thoracic Society (AnnalsATS), 2025.

 

May 5, 2025 – May 9, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Jared Erwin, PhD
Thursday, May 15th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present In-Person

Title:
Scaling standards based health care data

Abstract:
Healthcare data standards have been successful in improving interoperability, both for clinical use cases as well as research. The accessibility of data has expanded its use cases beyond the originally intended scope of the standards. This talk explores the challenges and opportunities of scaling standards-based healthcare software to support large-scale data storage, analytics, and secure access. Drawing from real-world implementations, we will examine patterns for scaling FHIR and OMOP, integrating with data lakes and distributed computing, and evolving approaches to security and identity management, including the roles of TEFCA and QHINs.

Speaker Bio:
Jared Erwin has a 20 year history developing healthcare software, from real-time embedded device to cloud based service.  Currently he develops the Microsoft Azure FHIR service.  He is also a lecturer at the University of Washington, teaching research methods.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

ANNOUNCEMENTS
­­­­­Have a new idea that you think will transform medicine? If so, be sure to review the details in our 2025 RFA and submit a proposal by May 23rd, 2025.

We are seeking applications for one-year pilot projects in the area of medical data science, for potentially up to $45K, with $25K guaranteed direct costs + the possibility of adding up to $20K in cloud credits, pending agreement with cloud service providers.

Please note that only UW faculty are eligible to apply. Learn more here.
_______________________
2025 UW Wellness Symposium
Enhance your well-being at the 2025 UW Wellness Symposium, Wednesday, May 21, and Thursday, May 22. Join expert-led virtual sessions on mental, physical and financial health with The Whole U.

UPCOMING EXAMS
Title: Computational Approaches to Predict Health Outcomes Using Cytometry Data
Student: Ya-Lin, Chen
Date/Time: Tuesday, May 13, 2025, 11 AM – 1 PM (Pacific Time)
Location: 850 Republican Street, Building C, Room 359
Zoom:  https://washington.zoom.us/j/92178550574

Abstract: Flow cytometry plays a pivotal role in medicine, as demonstrated by the widespread use of the complete blood count (CBC) across medicine. Alongside summary markers such as cell counts, CBCs also generate rich single-cell datasets, which are drastically underused. To allow for enhanced clinical use of these single-cell data streams, there is a need for methods which can generate clinically relevant biomarkers, or which can directly use these data for clinical prediction. To tackle these issues, we propose three primary research aims: (1) algorithmically developing novel markers from clinical single-cell data using unsupervised techniques, (2) evaluate and develop novel single-cell data modeling methods for predicting health outcomes, and (3) applying these single-cell approaches to significant health conditions, including predicting pregnancy and cardiac surgery complications and predicting treatment efficacy for hematologic malignancies. Approaches will be validated using large-scale data archives from University of Washington Medical Center (UWMC), alongside multiple open-source single-cell datasets. Collectively, this project will lead to generation of novel computational methods which can create tangible benefit for multiple well-defined clinical challenges.

April 28, 2025 – May 2, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Dr. Thinh Tran, PhD
Thursday, May 8th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present via Zoom

Title:
Assessing performance of large language models in clinical data abstraction and reasoning from unstructured clinical text

Abstract:
Patient health records contain a wide array of clinical information of high value. However, this information often resides in unstructured notes, making systematic computational analysis challenging and posing a major bottleneck for precision medicine. Recent advancements in knowledge extraction and reasoning have positioned large language models (LLMs) as a promising automated approach for clinical information extraction and inference. Here, we systematically evaluate the effectiveness of various LLMs by building a flexible and scalable software pipeline. Our pipeline ingests unstructured medical records and extracted specified clinical outputs, compatible with all mainstream commercial and open weight LLMs in a HIPAA-compliant manner. Using a human-annotated clinical note dataset of 843 unstructured medical records, we employed various LLMs for two key tasks: 1) extracting clinical information of cancer medication name and treatment duration; and 2) inferring treatment response based on longitudinal clinical notes and minimal residual disease (MRD) measurements. For information extraction, we evaluated Llama 3.3, Claude 3.5 Sonnet, GPT-4o, and DeepSeek-R1-8B, using an array of prompting strategies. Claude 3.5 achieved the highest accuracy for both medication name (F1=0.72) and treatment duration (median difference of 0 and 37 days for start and end date, respectively), but at a higher cost compared to other LLMs. Notably, most LLMs (except Llama 3.3) performed best with simple prompting that directly states the information to be abstracted, showing minimal gains from additional instructions or examples. For reasoning, we assessed both general-purpose LLMs (Claude 3.5, GPT-4o) and reasoning-focused LLMs (DeepSeek-R1, GPT-o1-mini, GPT-o3-mini) on their ability to infer treatment response by integrating clinical notes, imaging results, and MRD measurements. Unlike in math and programming where reasoning LLMs significantly outperform regular LLMs, both categories of LLMs demonstrated comparable capabilities in identifying and integrating relevant clinical evidence, even though the final conclusions varied by each method. These preliminary results suggested future improvements by reasoning LLMs and test-time scaling in the highly-specific clinical domain. Our results demonstrate the clear operational value of LLMs as a rapid, cost- and time-efficient means to comprehend clinical information for scientific advances and ultimately improved patient care.

Speaker Bio:
Thinh Tran is a Bioinformatics Data Scientist at Natera, where she applies AI/ML methods to clinical and genomic data to advance therapeutic discovery. She completed her PhD in Cancer Biology at Memorial Sloan Kettering Cancer Center, focusing on natural language processing for treatment extraction from clinical notes and on benchmarking variant effect predictors on annotating cancer variants of unknown significance and identifying novel prognostic biomarkers.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

ANNOUNCEMENTS
On May 1 2025, Doug Schaad will have completed 50 years of employment with the UW School of Medicine!

_______________________
By Monday, May 5th, please RSVP for BIME’s end-of-year celebration, where we recognize our students, postdocs, fellows, and great teachers and mentors.
Event Details

Friday, June 13th
2-3 pm: Recognition ceremony in Orin Smith Auditorium or via Zoom
3-5 pm: Reception in C123

Students, postdocs, and fellows are welcome to bring guests. Please indicate the number of in-person guests on the RSVP form.
A Zoom link for the hybrid recognition ceremony will be sent at a later date.
We hope you can join us!

­­­­­­­­_______________________
Have a new idea that you think will transform medicine? If so, be sure to review the details in our 2025 RFA and submit a proposal by May 23rd, 2025.

We are seeking applications for one-year pilot projects in the area of medical data science, for potentially up to $45K, with $25K guaranteed direct costs + the possibility of adding up to $20K in cloud credits, pending agreement with cloud service providers.

Please note that only UW faculty are eligible to apply. Learn more here.

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UPCOMING WORKSHOP
Considering Cultural and Linguistic Diversity in AI Applications: A Hybrid Workshop

AI has tremendous potential to improve all different aspects of our lives – leading to changes in many facets of life, including making our day-to-day lives easier, allowing for more personalized healthcare, and generating insights as we live and learn. There are also challenges and potential issues that we should consider, such as:

  • How are people affected differently by AI?
  • How might aspects of one’s culture, background, and other characteristics or traits affect how we might respond to AI?
  • How might these considerations inform how we regard or develop applications involving AI?
  • How do we collaboratively work towards integrating the values and preferences of communities of interest in AI tools?

This workshop will be a hybrid event on May 6, 2025 from 12:30pm to 16:30pm PST (time zone converter here). It seeks to bring together researchers, practitioners, and others who work with (or are interested in working with) culturally and linguistically diverse (CALD) populations. All levels of technical expertise welcome!

Event purposes:

  1. Raise awareness about the importance of thoughtful engagement in the design of AI applications for culturally and linguistically diverse populations;
  2. Offer a venue to engage in activities to think about relevant concepts and build relevant skills; and
  3. Present and hear about the work of others on this topic.

Workshop locations: University of Washington (UW); University of California, Irvine (UCI); and virtual.
Event Sponsors:

  • Association for Information Science & Technology (ASIS&T) U.S. West Chapter
  • UCI Connected Learning Lab

Accepted work including UW BIME folks here: https://cald-ai.github.io/accepted-works/.
More information and how to register here: https://cald-ai.github.io/
Contact Annie T. Chen <atchen@uw.edu> with questions.
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2025 UW Wellness Symposium
Enhance your well-being at the 2025 UW Wellness Symposium, Wednesday, May 21, and Thursday, May 22. Join expert-led virtual sessions on mental, physical and financial health with The Whole U.

PAPERS & PUBLICATIONS

  • Oliver Li. “Quantifying Controversy: A Novel Approach to Detecting Misinformation” has been accepted to the conference and proceedings at MEDINFO 2025 in Taipei, 11 Aug 2025.
  • Chunyan Duan, Shijun Chen, Jiajie Wang, Qianqian Tong, Qiantuo Liu, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Stephen R. Bowen .”Two-Stage Clustering and Auto Machine Learning to Predict Chemoradiation Response in Tumor Subregions on FDG PET for La-NSCLC” has been accepted as a SNAP ORAL PRESENTATION in the session “Radiopharmaceuticals, Theranostics, and Nuclear Medicine SNAP Oral: Therapy III” for the 67th Annual Meeting & Exhibition of the American Association of Physicists in Medicine in Washington, DC, July 27 – 30, 2025.
  • Chunyan Duan, Han Zhou, Jiajie Wang, Qiantuo Liu, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Stephen R. Bowen.“Muilt-Instance Learning Model with 2D and 3D Features Representation and Transformer-Based Prediction for FDG PET Tumor Chemoradiation Response of La-NSCLC” has been accepted as a General Poster Discussion Poster Presentation for the 67th Annual Meeting & Exhibition of the American Association of Physicists in Medicine in Washington, DC, July 27 – 30, 2025.
  • Chunyan Duan, Jing Sun, Jiajie Wang, Qiantuo Liu, Xiaojing Zhu, Faisal Yaseen, Shouyi Wang, Daniel S. Hippe, Stephen R. Bowen.“A Two-Layer, Two-Task Prediction Model Based on 3D Imaging and Residual Networks for Mid-Chemoradiation Tumor Response Prediction on FDG PET for La-NSCLC” has been accepted as a General Poster Discussion Poster Presentation for the 67th Annual Meeting & Exhibition of the American Association of Physicists in Medicine in Washington, DC, July 27 – 30, 2025.

UPCOMING EXAMS
Title: Computational Approaches to Predict Health Outcomes Using Cytometry Data
Student: Ya-Lin, Chen
Date/Time: Tuesday, May 13, 2025, 11 AM – 1 PM (Pacific Time)
Location: 850 Republican Street, Building C, Room 359
Zoom:  https://washington.zoom.us/j/92178550574

Abstract: Flow cytometry plays a pivotal role in medicine, as demonstrated by the widespread use of the complete blood count (CBC) across medicine. Alongside summary markers such as cell counts, CBCs also generate rich single-cell datasets, which are drastically underused. To allow for enhanced clinical use of these single-cell data streams, there is a need for methods which can generate clinically relevant biomarkers, or which can directly use these data for clinical prediction. To tackle these issues, we propose three primary research aims: (1) algorithmically developing novel markers from clinical single-cell data using unsupervised techniques, (2) evaluate and develop novel single-cell data modeling methods for predicting health outcomes, and (3) applying these single-cell approaches to significant health conditions, including predicting pregnancy and cardiac surgery complications and predicting treatment efficacy for hematologic malignancies. Approaches will be validated using large-scale data archives from University of Washington Medical Center (UWMC), alongside multiple open-source single-cell datasets. Collectively, this project will lead to generation of novel computational methods which can create tangible benefit for multiple well-defined clinical challenges.

 

April 21, 2025 – April 25, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Dr. Halil Kalicoglu, PhD
Thursday, May 1st – 11-11:50 am
Zoom Information: https://washington.zoom.us/my/bime590
Speaker will present via Zoom Only

Title:
Enhancing Rigor and Integrity of Biomedical Research using Natural Language Processing

Abstract:
There has been much debate about rigor, transparency, and reproducibility of biomedical research in recent years. Ongoing efforts aim to address issues in research conduct and reporting by developing standards, guidelines, and recommendations. As biomedical research output increases exponentially, automated tools are needed to complement such efforts and assist the stakeholders (e.g., researchers, journals, peer reviewers, funders, policymakers) in assessing and improving research output efficiently.  In this talk, I will discuss how NLP methods can help improve rigor and transparency in biomedical communication, as reflected in textual artifacts such as protocols, manuscripts, and publications. I will then present two research projects that we have been pursuing in this area: a) evaluating the methodological and reporting quality of clinical trial publications, and b) assessing citation integrity in biomedical literature. Finally, I will highlight some of the key challenges for NLP in this application domain.

Speaker Bio:
Dr. Halil Kilicoglu is Associate Professor in the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign. He specializes in natural language processing (NLP), artificial intelligence/machine learning (AI/ML), and knowledge representation with biomedical applications. His work aims to extract and organize knowledge buried in textual artifacts to benefit biomedical discovery and scholarship, and improve healthcare outcomes. His recent work includes development of automated methods to assess research rigor, transparency, and integrity of biomedical publications. Prior to joining the iSchool faculty in 2019, he was a research scientist at the U.S. National Library of Medicine, National Institutes of Health (NLM/NIH), where he led the Semantic Knowledge Representation project. His research has been funded by NIH, AHRQ, and HHS Office of Research Integrity.

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

ANNOUNCEMENTS
UPCOMING WORKSHOP
Considering Cultural and Linguistic Diversity in AI Applications: A Hybrid Workshop

AI has tremendous potential to improve all different aspects of our lives – leading to changes in many facets of life, including making our day-to-day lives easier, allowing for more personalized healthcare, and generating insights as we live and learn. There are also challenges and potential issues that we should consider, such as:

  • How are people affected differently by AI?
  • How might aspects of one’s culture, background, and other characteristics or traits affect how we might respond to AI?
  • How might these considerations inform how we regard or develop applications involving AI?
  • How do we collaboratively work towards integrating the values and preferences of communities of interest in AI tools?

This workshop will be a hybrid event on May 6, 2025 from 12:30pm to 16:30pm PST (time zone converter here). It seeks to bring together researchers, practitioners, and others who work with (or are interested in working with) culturally and linguistically diverse (CALD) populations. All levels of technical expertise welcome!

Event purposes:

  1. Raise awareness about the importance of thoughtful engagement in the design of AI applications for culturally and linguistically diverse populations;
  2. Offer a venue to engage in activities to think about relevant concepts and build relevant skills; and
  3. Present and hear about the work of others on this topic.

Workshop locations: University of Washington (UW); University of California, Irvine (UCI); and virtual.
Event Sponsors:

  • Association for Information Science & Technology (ASIS&T) U.S. West Chapter
  • UCI Connected Learning Lab

Accepted work including UW BIME folks here: https://cald-ai.github.io/accepted-works/.
More information and how to register here: https://cald-ai.github.io/
Contact Annie T. Chen <atchen@uw.edu> with questions.

 

2025 UW Wellness Symposium
Enhance your well-being at the 2025 UW Wellness Symposium, Wednesday, May 21, and Thursday, May 22. Join expert-led virtual sessions on mental, physical and financial health with The Whole U.

UPCOMING EXAMS
Title: Computational Approaches to Predict Health Outcomes Using Cytometry Data
Student: Ya-Lin, Chen
Date/Time: Tuesday, May 13, 2025, 11 AM – 1 PM (Pacific Time)
Location: 850 Republican Street, Building C, Room 359
Zoom:  https://washington.zoom.us/j/92178550574

Abstract: Flow cytometry plays a pivotal role in medicine, as demonstrated by the widespread use of the complete blood count (CBC) across medicine. Alongside summary markers such as cell counts, CBCs also generate rich single-cell datasets, which are drastically underused. To allow for enhanced clinical use of these single-cell data streams, there is a need for methods which can generate clinically relevant biomarkers, or which can directly use these data for clinical prediction. To tackle these issues, we propose three primary research aims: (1) algorithmically developing novel markers from clinical single-cell data using unsupervised techniques, (2) evaluate and develop novel single-cell data modeling methods for predicting health outcomes, and (3) applying these single-cell approaches to significant health conditions, including predicting pregnancy and cardiac surgery complications and predicting treatment efficacy for hematologic malignancies. Approaches will be validated using large-scale data archives from University of Washington Medical Center (UWMC), alongside multiple open-source single-cell datasets. Collectively, this project will lead to generation of novel computational methods which can create tangible benefit for multiple well-defined clinical challenges.

 

April 14, 2025 – April 18, 2025

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Daniel S. Weld, PhD
Thursday, April 24th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Speaker will present in-person

Title:
Intelligence Augmentation for Scientific Researchers

Abstract:
Recent advances in Artificial Intelligence are powering revolutionary interactive tools that will transform the very nature of the scientific enterprise, leading to increasingly automated scientific discovery. We describe several large-scale projects at the Allen Institute for AI aimed at developing open models, agentic platforms, and novel interaction that amplify the productivity of scientists and engineers.

Speaker Bio:
Daniel S. Weld is Chief Scientist and General Manager of Semantic Scholar at the Allen Institute of Artificial Intelligence and Professor Emeritus at the University of Washington. After formative education at Phillips Academy, he received bachelor’s degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator’s award in 1989, an Office of Naval Research Young Investigator’s award in 1990; he is a Fellow of the Association for Artificial Intelligence (AAAI), the American Association for the Advancement of Science (AAAS), and the Association for Computing Machinery (ACM). Dan was a founding editor for the Journal of AI Research, was area editor for the Journal of the ACM and on the editorial board for the Artificial Intelligence journal. Weld is a Venture Partner at the Madrona Venture Group and has co-founded several companies, including Netbot (sold to Excite), Adrelevance (sold to Media Metrix), and Nimble Technology (sold to Actuate).

BIME 591
Wednesdays – 11:30-12:20 pm
Section B, HSEB 421
Zoom Information: https://washington.zoom.us/my/velvinfu?pwd=Y3dJbjNBeFpzTC9HTXV1UDFYYXlKQT09 

PAPERS & PUBLICATIONS
Luna Xingyu Li, Ray-Yuan Chung, Feng Chen, Wenyu Zeng, Yein Jeon, Oleg Zaslavsky, “Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design”, accepted by the CALD-AI workshop “Considering Cultural and Linguistic Diversity in AI Applications” at ASIS&T.

ANNOUNCEMENTS
2025 UW Wellness Symposium
Enhance your well-being at the 2025 UW Wellness Symposium, Wednesday, May 21, and Thursday, May 22. Join expert-led virtual sessions on mental, physical and financial health with The Whole U.