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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

October 21 – October 25, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Yeon Mi Hwang, PhD
Thursday, October 31st – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Presenter will present remotely

Leveraging Real-World Data, Including the Electronic Health Record, to Fill in Clinical Knowledge Gaps in Treating Pregnant People
Abstract:
Pregnant individuals are often excluded from clinical trials, leaving a critical knowledge gap in understanding the safety and efficacy of medical treatments during pregnancy. Despite this, many pregnant people take prescription medications. Electronic health records (EHRs), with rich longitudinal data, offer an opportunity to investigate these treatments.

In this talk, I present my research addressing this gap through the analysis of EHR data across four key areas. First, I developed a workflow using propensity score matching to detect drug effect signals associated with preterm birth. This was followed by a validation of these signals, focusing on the link between Selective Serotonin Reuptake Inhibitors and preterm birth risk. Next, I examined the clinical application of COVID-19 antithrombotic guidelines for pregnant women, uncovering surprisingly low prophylactic anticoagulant prescription rates. Finally, I explored the impact of immune-mediated inflammatory diseases on pregnancy outcomes, revealing a heightened risk of adverse outcomes dependent on disease type and comorbidities.

While EHRs provide invaluable insights, the research faced challenges such as variable misclassification and data quality issues, which I will discuss along with potential solutions. This work aims to advance understanding of pregnancy treatments, with particular focus on preterm birth and broader maternal-fetal health outcomes.

Presenter bio: Yeon Mi Hwang, PhD is a postdoctoral researcher at Stanford University, specializing in biomedical informatics with a focus on health equity. Her research leverages real-world data to address knowledge gaps in treating vulnerable populations, particularly pregnant individuals and older adults. With expertise in hypothesis-driven and machine learning methods, she investigates treatments and outcomes in diverse populations, aiming to improve care through data-driven insights.

PAPERS & PUBLICATIONS
Riley BC, Phuong J, Hasan RA, Stansbury LG, Hess JR, Roubik DJ. Expired blood transfusion and mortality outcomes in combat trauma patients. Transfusion. 2024; 64(9): 1683–1691. https://doi.org/10.1111/trf.17943

GENERAL EXAM
Title: Utilizing contextual embeddings from language models for the automatic detection of depression symptom severity and suicide risk
Student: Xinyang Ren
Date/Time: October 30th at 3 pm
Location: South Campus Center, Room 322
Zoom: https://washington.zoom.us/my/cohenta

Abstract: Mental health conditions such as depression are serious public health problems globally and can carry an increased risk of adverse outcomes including suicide. Compounding this issue is a persistent shortage of mental health professionals, leading to a critical need for innovative solutions to expand their capacity. Natural language processing (NLP) has emerged as a valuable tool for analyzing psychological texts to uncover relationships between language and mental states, particularly in the areas of detecting depression symptom severity and suicide risk. This research explores the potential of utilizing contextual embeddings from neural language models to enhance the detection of mental health indicators in two relatively under-explored patient-generated data sources: text-based therapy messages and internet search logs. Specifically, Aim 1 concerns assessment of the utility of contextual embeddings of first-person singular pronouns as predictors of depression symptom severity. Aim 2 is focused on detecting web search pattern changes and abnormal search behaviors in periods of high suicide risk. Aim 3 involves evaluation of the utility of contextual representations of web searches in predicting suicidal ideation. In summary, this research aims to leverage neural language representation methods to advance the automatic detection and prediction of depression symptom severity and suicide risk, with the potential to improve mental health care outcomes through automated monitoring of risk and responsiveness.

October 14 – October 18, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Erik G. Van Eaton, MD, FACS
Thursday, October 24th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Presenter will be in-person

Title: Clinical Decision Support Systems and FDA Final Guidance: What Now?
Abstract: There is increasing interest in, and spending on, Clinical Decision Support Systems (CDSS) among US health systems. Some studies have proposed expanded adoption of CDSS will reduce the cost of healthcare and improve quality. The addition of machine learning and other artificial intelligence tools to CDSS systems seems likely in the coming years. Among the considerations for CDSS creators – apart from the clinical reliability of their outputs – are usability, compliance, and regulation. From this talk, you’ll learn about the current FDA regulatory environment surrounding CDSS. We will discuss CDSS proposals underway nationally and how they might be viewed by the FDA in light of the agency’s 2022 Final Guidance document. You’ll hear the opinion of a CDSS creator about the impact of the FDA Final Guidance on third-party CDSS tools.
Presenter bio: Erik G. Van Eaton, MD, FACS, is an Associate Professor of Surgery and Surgical Critical Care, and an Adjunct Associate Professor of Biomedical Informatics and Medical Education at the University of Washington and Harborview Medical Centers, in Seattle, Washington. Dr. Van Eaton specializes in Trauma Surgery, Surgical Critical Care, Emergency General Surgery, Acute Care Surgery, and General Surgery. As part of his General Surgery practice, Dr. Van Eaton’s clinical focus is complex hernia operations, surgical treatment of fistula, intestinal surgery, gallbladder surgery, and reconstructive abdominal procedures after recovery from major trauma A trained medical informatics scientist, Dr. Van Eaton studies how Electronic Health Record (EHR)-embedded software can support core clinical workflows. Research avenues include observational studies of EHR use in physician handoffs, subjective evaluations of EHR-related burnout and stress, and impact of EHR-embedded software and mobile apps on quality metrics. Dr. Van Eaton builds collaborative relationships among successful clinical informatics projects health systems throughout the world to bring high-performance clinical information management to bedside decisions. As an NIH NLM Fellow at UW, Dr. Van Eaton developed a computerized rounding and sign-out software system that changed the ways in which residents communicate about, and manage “ownership” of, their patients. This work led to a spin-out healthcare information technology company from the University of Washington in 2011 called TransformativeMed Inc.

PAPERS & PUBLICATIONS

  • Ray-Yuan Chung, MPH, RD, Feng Chen MS, Yein Jeon, MS, Oleg Zaslavsky, PhD. Improve Technology and eHealth Literacy in a Retirement Community via The Use of LLM-Powered Chatbot. Poster presentation at the Geological Society of America 2024 Annual Scientific Meeting. Seattle, WA.
  • Dr. Jim Phuong was part of a panel of coauthors that received a manuscript acceptance to the AMIA Annual Symposium 2024 as a Conference Proceedings. The manuscript explored the utility of conceptual mapping requirements in Kidney biospecimen research via ontological linkages across multiple data repositories. As part of a collaborative effort within the Kidney Precision Medicine Project (KPMP), Kidney Mapping Atlas Project (KMAP), and the Human BioMolecular Atlas Project (HuBMAP). Dr. Alexander Diehl will be presenting the paper at AMIA.

The He et al. 2024 manuscript received a nomination for LEIAF Best Paper Award! The award ceremony will be presented at the 2024 AMIA Annual Symposium Academic Forum Business Session.

  • He YO, Barisoni L, Rosenberg AZ, Robinson PN, Diehl AD, Chen Y, Phuong J, Hansen J, Herr BW, Börner K, Schaub J. Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine. bioRxiv. 2024 Apr 2:2024-04. https://doi.org/10.1101/2024.04.01.587658
  • Dr. Jim Phuong was part of a panel of co-authors that received a manuscript acceptance with Pediatric Anesthesia. The manuscript explored coagulopathy across different age groups of Pediatric trauma cases. This work is part of a collaboration within the Harborview Injury Prevention Research Center (HIPRC) Trauma Transfusion Research Interest Group (TTRIG).
  • Deshpande SJ, Tsang HC, Phuong J, Hasan R, Liu Z, Stansbury LG, Hess JR, Vavilala MS. Trauma-induced coagulopathy across pediatric age groups: A retrospective cohort study evaluating testing and frequency. Pediatr Anesth. 2024; 00:1-9. [Accepted 8 Oct 2024].

October 7 – October 11, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Brian Piening, PhD
Thursday, October 17th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

2024 UW Medical AI & Data Science Journal Club
Monday, October 14, 2024 01:00 PM Pacific Time
Yue Wu, PhD, will discuss Segment Anything and  Segment anything in medical images
In person: F107, 750 Republican St, Seattle 98109
-or-
Join via Zoom: https://washington.zoom.us/j/95988022322?pwd=OWxUeFhSZHZaUklUdmRtcnUwY2VqUT09
Meeting ID: 959 8802 2322
Passcode: 464933

PAPERS & PUBLICATIONS
Zoljargal (Zoey) Lkhagvajav will present “From implementation to definition of national strategy” at the workshop titled “Towards gender transformative approaches for the Implementation of digital health solutions and the Definition of digital health strategies” at the 2024 Conference of the Global South e-Health Observatory (ODESS) in Lavaur, France.

September 30 – October 3, 2024

UPCOMING LECTURES AND SEMINARS

BIME 590
Presenter: Jimmy Phuong, MSPH, PhD – More information coming next week!
Thursday, October 10th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

PAPERS & PUBLICATIONS
Bedmutha MS Bascom E, Sladek K, Tobar K, Casanova-Perez R, Andreiu A, Bhat A, Mangal S, Wood BR, Sabin J, Pratt W, Weibel N, Hartzler AL.  AI-generated feedback on social signals in patient-provider communication: Technical performance, feedback usability, and impact. JAMIA Open. In Press.

Chen F, Bedmutha MS, Chung R, Sabin J, Pratt W, Wood BR, Weibel N, Hartzler AL, Cohen T. Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations. AMIA 2024 Annual Symposium. San Francisco, CA. In press

Langevin R, Mohanraj D, Shah L, Sabin J, Wood BR, Pratt W, Weibel N, Hartzler AL. Envisioning the Future of Primary Care: Intervention Strategies to Support Patient-Centered Communication Feedback Technology. Podium Abstract presentation at AMIA 2024 Annual Symposium, San Francisco, CA. In press.

Rosenfeld M, Berlinski A, Sawicki G, Nguyen-Kearns E, Fogarty B, Zappone-Case B, Hartzler AL. Incorporating the perspectives of participants and research coordinators on home spirometry into clinical trial design: The example of the OUTREACH study. Journal of Cystic Fibrosis. 2024 Jul 1;23(4):739-43. PMID: 39079878

Burdis N, Kapnadak SG, Bartlett LE, McElvaney O, Milinic T, Wai TH, Lange AV, Reid N, Dunitz JM, Billings JL, Pilewski JM, Saavedra M, Goss C, Hartzler AL, Ramos KJ. Attitudes toward and preparedness for lung transplantation among individuals with cystic fibrosis in the era of highly effective modulators. BMC Pulmonary Medicine. 2024 Jul 18;24(1):348. PMID: 39026320

ANNOUNCEMENT
Members from the UnBIASED Research team will present a workshop Tuesday 10/29 9-10am at the 2nd Annual Conference on “Challenging Norms: Uplift anti-racist work to transform the landscape of medical education.” This virtual Conference is hosted by Mt Sinai Monday 10/28-Wed 10/30 8:45-2pm each day. Registration is free!

Register here:
https://www.accelevents.com/e/Challenging-Norms-Uplifting-Anti-Racist-Work-to-Transform-the-Landscape-of-Medical-Education–2nd-Annual-Conference#about

September 23 – September 27, 2024

PAPERS & PUBLICATIONS
Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai M. Zhang, Lanyi Zhu, Mike A. Merrill, Jeffrey Heer, Tim Althoff. BLADE: Benchmarking Language Model Agents for Data-Driven Science. Accepted to EMNLP Findings. arXiv preprint: https://arxiv.org/abs/2408.09667

Roesler, D., Johnny, S., Conway, M., Chen, A. T. (2024). Leveraging contextual embeddings with affective, social, and behavioral features for substance use stigma detection. BMC Digital Health, 2(60), 1-18. DOI: 10.1186/s44247-024-00065-0

Wong, S. H., Chen, A. T. (accepted). Self-actualization in the era of social media algorithms: Soul-searching in a “soulless” landscape. Paper to be presented at Special Interest Group for Social Informatics (SIG-SI) Research Symposium, co-located with the annual meeting of the Association for Information Science & Technology (ASIS&T) 2024, Oct. 25-29, 2024, Calgary, Canada.

ANNOUNCEMENT
Andrea Hartzler, Bill Lober, & Meliha Yetisgen will be inducted into the American College of Medical Informatics (ACMI) as fellows. For more information, see the press release. Congratulations!

September 16 – September 20, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590

Speaker will be in-person
Presenter: Peter Tarczy-Hornoch, MD, FACMI
Thursday, September 26th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590

Title: The Department of Biomedical Informatics Vision, History, Strategic Plan and Praxis

Abstract: The presentation will provide an overview of the Department of Biomedical Informatics and Medical Education through the lens of the current strategic plan. The overview will include vision, history and evolution looking at the synergy between research, education and practice (praxis) as well as the synergy between practice, applied research and foundational research. Within each area (research, praxis, education) current activities and future plans will be reviewed.

Presenter Bio: Peter Tarczy-Hornoch has over 40 years of experience in computer science, over 35 years in biomedical informatics and 20 years in clinical medicine (pediatrics and neonatology). He has been at the University of Washington since 1992, serving as Head of the Division of Biomedical and Health Informatics since 2001 and serving as Chair of the Department of Biomedical Informatics and Medical Education since 2011. He has served in a variety of operational leadership roles in UW Medicine IT Services since 1992 in the analytics, research, and clinical computing domains, currently (since January 2022) serving as UW Medicine Chief Data Officer. He has played a leadership role in the creation and evolution of the BIME educational programs (undergraduate (joint with iSchool), MS/PhD, postdoctoral, applied clinical informatics MS (CIPCT) joint with Nursing, Clinical Informatics Fellowship joint with Family Medicine). He has led a number of key initiatives in informatics practice (praxis) including the areas of telemedicine, digital library, electronic medical records, data warehousing, analytics, clinical research informatics). His unifying theme of research over the last two decades has been data integration of electronic biomedical data (clinical, genomic and other including data) both for a) knowledge discovery and b) in order to integrate this knowledge with clinical data at the point of care for decision support. His current research focuses on a) secondary use of electronic medical record (EMR) for translational research including outcomes research, learning healthcare systems, patient accrual and biospecimen acquisition based on complex phenotypic eligibility criteria, b) the use of EMR systems for cross institutional comparative effectiveness research, and c) integration of genomic data into the EMR for clinical decision support.

PAPERS & PUBLICATIONS
A Digital Health Landscape Assessment was carried out for the first time in Mongolia. The report is available at https://sudalgaa.gov.mn/pdf/digital-health-landscape-assessment-mongolia-report-szl.  Zoljargal (Zoey) Lkhagvajav contributed as a co-author, with support from Jared Erwin.

Marini M, Sabin J, O’Shea B and Vianello M. (2024). Editorial: Implicit social cognition: malleability and change. Front. Psychol. 15:1475986.doi: 10.3389/fpsyg.2024.1475986. This is an editorial from the 4 editors of a special edition in Frontiers of Psychology.

Brian C. Chang, Jonathan Renslo, Qifei Dong, Sandra K. Johnston, Jessica Perry, David R. Haynor, Gang Luo, Nancy E. Lane, Jeffrey G. Jarvik and Nathan M. Cross (2024). Using an Ensemble of Segmentation Methods to Detect Vertebral Bodies on Radiographs. American Journal of Neuroradiology, August 2024. https://www.ajnr.org/content/early/2024/08/29/ajnr.A8343

ANNOUNCEMENT
Faisal Yaseen was awarded AMIA’s LEAD Fund Trainee & Early Career Scholarship. Information for 2024 awards can be found here. Congratulations!

 

August 19 – August 23, 2024 

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/26/2024!

PAPERS & PUBLICATIONS
Yujuan Velvin Fu, Giridhar Kaushik Ramachandran, Ahmad Halwani, Bridget T. McInnes, Fei Xia, Kevin Lybarger, Meliha Yetisgen, and ̈Özlem Uzuner. CACER: Clinical concept annotations for cancer events and relations. Accepted by Journal of the American Medical Informatics Association, 2024.

Yein Jeon & Thomas Payne. Machine Learning in Time-to-Event Prediction of Ventricular Arrhythmias among Older Adults with Type 2 Diabetes and Coronary Artery Disease has been accepted for poster presentation at American Heart Association (AHA) conference, Annual Scientific Sessions, Chicago, IL, 2024.

August 5 – August 9, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/26/2024!

PAPERS & PUBLICATIONS
Faisal Yaseen presented “Voxel Forecast Classifier Based on Kd-Net for Mid-Chemoradiation Intra-Tumoral Response Prediction on FDG PET for La-NSCLC” at the 66th Annual Meeting & Exhibition of the American Association of Physicists in Medicine in Los Angeles, CA, 2024.

Zhou, J., Cole, C. L., Chen, A. T. (accepted). Basreh or Basra? Geoparsing historical locations in the Svoboda Diaries. Association of Computational Linguistics 2024 Student Research Workshop, Aug. 11-16th, 2024, Bangkok, Thailand.

An, J., Fan, W., Mittal, A., Zhang, Y., & Chen, A. T. (accepted). Mobile app use among persons with fibromyalgia: A cross-sectional survey. Journal of Pain. DOI: 10.1016/j.jpain.2024.03.011

O’Brien, H., Chen, A. T. Kaneshiro, K., Zaslavsky, O. (accepted). Designing for digital health engagement: Lessons learned from the Virtual Online Communities for Aging Life Experience (VOCALE) Intervention. Interacting with Computers. DOI: 10.1093/iwc/iwae030

Chen, A. T., Child, C. E., Asirot, M. G., Domoto-Reilly, K., & Turner, A. M. (accepted). A visual approach to facilitating conversations about supportive care options in the context of cognitive impairment. Journal of Biomedical Informatics. DOI: 10.1016/j.jbi.2024.104691

ANNOUNCEMENT
Forbes just published their list of America’s Best Employers for Women in 2024.  Guess who was:

  • #1 of all healthcare organizations in Washington, Wyoming, Alaska, Montana, Idaho, and Oregon?
  • #9 of all healthcare organizations in the country?
  • #25 of all companies in the entire nation?

Wow!  UW Medicine ranked #25 of all companies in America!  Here is a link to the story:  Forbes America’s Best Employers for Women 2024 List

July 15 – July 19, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/26/2024!

PAPERS & PUBLICATIONS
Groves, P. S., Farag, A., Perkhounkova, Y., Sabin, J. A., Witry, M. J., & Wright, B. (2024). Nurse judgements of hospitalized patients’ safety concerns are affected by patient, nurse and event characteristics: A factorial survey experiment. Journal of Clinical Nursing, 00, 1–14. https://doi.org/10.1111/jocn.17372

Zhaoyi Sun, Sitong Liu, Wenyu Zeng, John Gennari. An Ontology-Guided Analysis to Unveil Disparities of Social History Documentation in Discharge Notes has been accepted for poster presentation for the AMIA 2024 Annual Symposium.

UPCOMING FINAL EXAMS
Title: A Longitudinal Study Based on Secondary Usage of Electronic Health Record for Identification of Erectile Dysfunction (ED) Risk Factors and Identification of Patients
Student: Tianran Li
Date/Time: 7/19 8:30-9:30am
Location: Zoom only
Zoom:   https://washington.zoom.us/j/4655089720

Abstract: Erectile Dysfunction (ED) affects one in five men in the United States and rises in prevalence with increasing age. Recognizing the chronic nature of ED and the lacking in comprehensive longitudinal healthcare documentation and multifactor analysis, this study utilized integrated and enriched electronic health records (EHR) from the electronic MEdical Records and GEnomics (eMERGE) cohort 3 at Kaiser Permanente/University of Washington (KPWA) site. We developed a novel method to systematically identify ED cases and employed statistical methods for survival analysis of risk factors and the longitudinal patterns associated with ED. This approach enhances our comprehension of disease progression, supports improved clinical management, and ultimately aims to elevate the quality of life and healthcare outcomes for affected individuals. Our study demonstrates the significant potential of integrated EHR-based informatics in advancing the understanding and management of complex chronic conditions like ED.

July 8 – July 12, 2024

PAPERS & PUBLICATIONS
Feng Chen, Manas Satish Bedmutha, Ray-Yuan Chung, Janice Sabin, Wanda Pratt, Brian R. Wood, Nadir Weibel, Andrea L. Hartzler, Trevor Cohen. Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations. Accepted for the AMIA 2024 Symposium.

Faisal Yaseen, Daniel S. Hippe,  Parth Vijaykumar Soni, Shouyi Wang, Chunyan Duan, Stephen R. Bowen.  Variogram Modeling of Spatially Variant Early Response to Concurrent Chemo- and Immunotherapy for Metastatic Non-Small Cell Lung Cancer has been accepted as a podium abstract for oral presentation for the AMIA 2024 Annual Symposium.

Faisal Yaseen, Luna Li, Raina H. Langevin. Enhancing Public Health Surveillance in King County: Applying Data Modernization to Social Determinants of Health has been accepted for poster presentation for the AMIA 2024 Annual Symposium.

Bhargav Vemuri, Peter Tarczy-Hornoch P, Jen Hadlock. Multivariate Longitudinal Trajectories of Response to GLP-1 RA Therapy has been accepted for a poster presentation for the AMIA 2024 Annual Symposium.

Faisal Yaseen, Murtaza Taj, Resmi Ravindran, Fareed Zaffar, Paul A. Luciw, Aamer Ikram, Saerah Iffat Zafar, Tariq Gill, Michael Hogarth, Imran H. Khan. An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data. Journal of Medical Primatology, https://doi.org/10.1111/jmp.12722

UPCOMING FINAL EXAMS
Title: Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs
Student: Qifei Dong
Date/Time: Friday, July 12 @ 11 am
Location: Zoom Only
Zoom Info: https://washington.zoom.us/j/4666998448?pwd=dXo1NjFCQkNJclFYc2Y0SHN3c0JPZz09

Abstract: Although osteoporosis is a debilitating disease that affects 9% of individuals over 50 years of age in the US and 200 million women globally, osteoporosis screening is underutilized. A complementary approach to osteoporosis screening is opportunistic screening using pre-existing images to detect spinal osteoporotic compression fractures (OCFs). Spinal OCFs are often incidental findings and under-reported. An automated opportunistic screening tool can ensure earlier diagnosis and treatment of spinal OCFs and osteoporosis. A crucial component for the automated opportunistic screening tool is an OCF classifier that detects OCF on each vertebral body. In this research, we focus on building this OCF classifier. To do this, two spine radiograph datasets were obtained, whose radiographs are in the Digital Imaging and Communications in Medicine (DICOM) format. To annotate the data, we designed DicomAnnotator, a configurable open-source software program for efficient DICOM image annotation. With the annotated radiographs, we used five deep learning algorithms to build the OCF classifier. Training a deep learning model on a large dataset is often time-consuming. During deep learning model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed. This makes the deep learning model training process more user-friendly. We designed the first set of techniques to support progress indication for deep learning model training that allows early stopping. In summary, we realized the following three aims in this research:

1)            Aim 1: Design DicomAnnotator. Usability evaluation shows that DicomAnnotator is easy to learn, is efficient to use, and allows annotators to quickly make several types of annotations on a large set of DICOM images.

2)            Aim 2: Build the OCF classifier. Model evaluation results show that our OCF classifier has some generalizability to clinical data and a suitable performance for our future opportunistic osteoporosis screening.

3)            Aim 3: Design progress indication methods for deep learning model training. Our experiments show that our progress indicator can offer useful information even if the run-time system load varies over time and can self-correct its initial estimation errors, if any, over time.

ANNOUNCEMENT
Hasan Ahmad, DO, MBA, MSc, FACP, has accepted a position as Associate Chief Medical Information Officer at Parkview Health in Fort Wayne, Indiana.

June 17 – June 21, 2024

UPCOMING
PAPERS & PRESENTATIONS
Andrea Hartzler was invited to represent work from the UnBIASED project at the “Human-centered AI Mini Conference at the Microsoft reactor on June 12th. The community event celebrated Pride month and attracted technologists, researchers, students, and enthusiasts from around the area.

UPCOMING EXAM
Final Exam
Title: Robust Methods for Clinical Text Classification and Disease Understanding with NLP Extracted Symptoms from Clinical Notes
Student: Weipeng Zhou
Date/Time: Tuesday, June 25 @ 10 am
Location: 750 Republican Street, Building F, Room 107
Zoom: https://washington.zoom.us/my/melihay

Abstract: Electronic Health Records (EHR) contain comprehensive medical and treatment histories of patients and have the potential to be used to provide better healthcare. A significant portion of the EHR is in the form of clinical notes and Natural Language Processing (NLP) methods can help extract hidden information from them. However, applying NLP in healthcare has challenges. Many of the clinical note datasets are scarce and imbalanced, making it difficult to develop generalizable and robust NLP methods. Additionally, effective use of NLP in healthcare requires close collaboration with medical experts to identify and understand meaningful clinical problems. This dissertation addresses these challenges and explores the application of NLP in healthcare. In Chapter 1 and 2, we develop generalizable and robust NLP methods for clinical note classification and female suicide report coding. In aims 3 and 4, we apply NLP to extract symptoms from clinical notes and study risk factors associated with out-of-hospital cardiac arrest (OHCA) and Long COVID.

ANNOUNCEMENTS
WSAS 2024 Symposium on AI for Washington State: Using Artificial Intelligence to Explore, Discover and Understand
Find more information here. Event is *free* for students. Keynote speaker is the President of NASEM.

June 10 – June 14, 2024

PAPERS & PRESENTATIONS
Trevor Cohen was a presenter at “Publishing NLP in JAMIA: Past, Present, and Future” on June 11, 2024,  where trends in publishing NLP work, including insights from the forthcoming focus issue on Large Language Models were discussed.

Sunan Cui, Jing Zeng, Daniel Hippe, Jie Fu, Faisal Yaseen, Yulun He, John Kang, Clemens Grassberger, Ramesh Rengan, Stephen Bowen. “Phase II Trial of Risk-Adaptive Chemoradiation for Unresectable Non-Small Cell Lung Cancer: FLARE-RT Mature Outcomes and Patterns of Failure” has been accepted for presentation in ORAL scientific session for the 66th annual meeting of American Society for Radiation Oncology (ASTRO).

UPCOMING EXAMS

Final Exams
Title: Reporting understandable, useful, and trustworthy results of clinical prediction model studies: insights from biomedical researchers
Student: Ivan Rahmatullah
Date/Time: Monday, June 17th, 1 pm
Location: Zoom only – https://washington.zoom.us/my/andreahartzler

Abstract:
Despite the increasing number of clinical prediction model (CPM) studies, the quality of reporting, especially for preimpact analysis studies focusing on developing and validating CPMs in research papers, remains subpar. This poor reporting quality hinders the progression of CPM studies by impeding follow-up studies, such as external validation, impact analysis studies, and systematic reviews. While the reporting guideline for these studies, TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), emphasizes transparency, biomedical researchers advocate for CPM study results to additionally embody three quality attributes: understandable, useful, and trustworthy. Yet, the extent to which biomedical researchers perceive and ensure that CPM study results meet these quality attributes, has not been explored
This dissertation aims to bridge these gaps by identifying challenges, needs, and visualization preferences among biomedical researchers to ensure CPM study results meet these three quality attributes. Each main chapter in this dissertation addresses a specific aim. Aim 1, presented in Chapter 4, uses a mixed-method survey to explore biomedical researchers’ challenges in ensuring that CPM study results meet the three quality attributes as authors and reviewers. Aim 2, detailed in Chapter 5, involves interviews with biomedical researchers to characterize their needs to ensure the three quality attributes in CPM study results. Aim 3, outlined in Chapter 6, based on interviews with biomedical researchers, identifies visualization preferences that could enhance the quality of CPM study results. The concluding Chapter 7 summarizes these findings and their contributions to biomedical informatics, which highlight a novel approach to improve the quality of CPM study results by focusing on the three quality attributes and engaging biomedical researchers beyond traditional expert panels.
Furthermore, the dissertation includes foundational chapters setting the research stage. Chapter 1 reviews relevant prior work and outlines my motivations for this study, rooted in my experiences as a primary care clinician and biomedical researcher. Chapter 2 reports on my preliminary work through a primary care provider survey about their use of clinical prediction rules. Chapter 3 describes recruitment strategies that enhanced biomedical researchers’ participation in the Chapter 4 survey, utilizing PubMed records for expanded outreach.

ANNOUNCEMENTS
Congratulations to IMDS Pilot Awardees!
Zoljargal (Zoey) Lkhagvajav was selected as an awardee for the 24-25 Institute of Medical Data Science pilot award program for her proposal “Data pipeline development for curation of Geriatric Trauma Outcome prognostication.” She will be working with Jim Phuong and Hamilton Tsang.

Yifan Wu, Trevor Cohen and Ian DeBoer were selected as awardees for their proposal entitled “Using temporality, dose effect, and co-medication to improve drug safety surveillance,” for a 2024-25 Medical Data Science Pilot Award. The proposal concerns the use of Transformer architectures, including variants that represent dose and lab information as continuous variables, to model electronic health record data for the purpose of post-marketing surveillance of pharmaceutical products.

CRBM Receives NIBIB Grant
National Institute of Biomedical Imaging and Bioengineering has committed a multi-million-dollar grant to support the Center for Reproducible Biomedical Modeling (CRBM) at the University of Washington. Under the leadership of UW Bioengineering Professor, Herbert Sauro, the grant will allow CRBM to continue its innovative work for an additional five years. This significant investment underscores the importance of the Center’s mission to enhance the reliability and reproducibility of biomedical models. John Gennari, along with PhD student Luna Li, will be a key contributor to the project.

Leveraging Data Standards for Improving Interoperability
July 9, 2024 | 1:00–2:00 p.m. ET
Join Austin Fitts, Pharm.D., Nikki Wood, D.O., David Noyd, M.D., M.P.H., and Wayne H. Liang, M.D., M.S., FAMIA, for a discussion on how to enhance interoperability with data standards, with an emphasis on the National Childhood Cancer Registry (NCCR), Childhood Cancer–Data Integration for Research, Education, Care, and Clinical Trials (CC-DIRECT), development of pediatric content within the HemOnc.org ontology standard, and creation of the minimal Common Oncology Data Elements (mCODE) pediatrics extension. Click here to register.

June 3 – June 7, 2024

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. LREC-COLING, 2024.   – Outstanding Paper Award

ANNOUNCEMENTS
Angad Singh was selected as the 2024 recipient of David Thorud Leadership Award as part of UW’s Awards of Excellence series. This award is the highest leadership honor at the University of Washington and is given to one faculty member across the University each year. Please see more information here.

May 27 – May 31, 2024

UPCOMING LECTURES AND SEMINARS
BIME 590 – See you in autumn quarter – 9/26/2024!

PAPERS & PRESENTATIONS
Ojas A. Ramwala, Kathryn P. Lowry, Nathan M. Cross, William Hsu, Christopher C. Austin, Sean D. Mooney, Christoph I. Lee, Establishing a Validation Infrastructure for Imaging-Based AI Algorithms Prior to Clinical Implementation, Journal of the American College of Radiology, 2024, ISSN 1546-1440 https://doi.org/10.1016/j.jacr.2024.04.027.

UPCOMING EXAMS
Final Exams
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
New $12.6M grant to help unravel hallucinations
UW Medicine researchers, Dr. Dror Ben-Zeev and Dr. Trevor Cohen have received a five-year, $12.6 million grant to create a predictive AI tool for people who experience hallucinations. Hallucinations are the most common psychotic experience, they occur in about 13% of the U.S. population. The new project, an app that will be given to 2,000 participants, is meant to help clinicians identify the severity of hallucinations and the need for a deeper level of care.

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.

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.