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Chair’s Message

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

Cordially,

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

Biomedical Informatics and Medical Education Newsletter

November 13 – November 17, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590
No Seminar on November 23rd. Back on November 30 with Patrick Wedgeworth!
Presenter: Patrick Wedgeworth, MD, MISM, CIPCT
Thursday, November 16th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person
Zoom Information: https://washington.zoom.us/my/bime590

Title: Using EHR Tools to Standardize Food Insecurity Referrals in Partnership with Service Providers 
Abstract: Almost 34 million individuals in the United States live in food insecure households.  Due to the impact of food insecurity on health, many healthcare organizations are beginning to screen for food insecurity and make referrals for interventions.  Many such referrals leverage tools within electronic health records (EHRs) to address food insecurity, for example electronic referrals to food pharmacies in primary care clinics.  However, there is very little research on what types of information that CBOs need from health systems to meet a patient’s needs.  The primary goal of my study is to understand the information needed for effective community-based organization (CBO) referrals and assess the efficacy of currently available EHR tools at communicating this information in a manner that is useful to the CBO receiving the referral. 
Presenter Bio: Dr. Patrick Wedgeworth is a graduate of the Clinical Informatics fellowship at University of Washington, and an Internal Medicine trained physician with a master’s degree in information systems management from Carnegie Mellon University.  he has experience in Epic building, data analytics, patient care in underserved communities, and working in an advisory role for municipal public health initiatives.  His work is focused at the intersection of clinical care, analytics and social determinants of health.

GENERAL EXAMS
Title: Toward a Unified Machine Learning Framework for Care Management Resource Allocation and Healthcare Quality Measurement: A Real-World Study in Asthma Management and Beyond
Student: Xiaoyi Zhang
Date/Time: November 29th, 2-4pm
Location: UW Medicine South Lake Union, Building C, Room 123A
Zoom: https://washington.zoom.us/j/4666998448?pwd=dXo1NjFCQkNJclFYc2Y0SHN3c0JPZz09
Meeting ID: 466 699 8448
Password: 524369
Abstract:
The application of machine learning to healthcare has significant demonstrated potential to improve both system efficiency and patient outcomes. By extracting meaningful patterns from vast amounts of data, these methods can infer patients’ health trajectories and provide informed intervention suggestions. In particular, our research investigates how to optimize the allocation of scarce preventive care resources for asthma management. Our prior work led to a machine learning model that predicts asthma hospital encounters. However, there are several challenges to implementing this model in real-world clinical settings:

Evaluation: Beyond accuracy metrics, two questions remain regarding our model’s performance. First, for a patient who will encounter asthma hospital visits in the future, how timely can our model identify the risk for the first time? Since any preventive intervention requires sufficient time to take effect, a model should identify the risk as early as possible to prevent poor outcomes. Second, if our model erroneously predicts a patient encounter for asthma in the future, how likely will the patient encounter ≥1 asthma hospital visit somewhere else or have ≥1 surrogate of a poor outcome? Those who have surrogates of poor outcomes are reasonable candidates for preventive interventions.
Interpretability:  Black-box machine learning models do not explain their predictions, forming a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model’s predictions and to suggest tailored interventions without sacrificing model performance. For an average patient, our explanation method can generate over 5,000 rule-based explanations. However, the user of the automated explaining function, often a busy clinician, needs to obtain the most useful information for a patient quickly by viewing just the top few explanations. Appropriately ranking these explanations generated for a patient is crucial for adoption of our automated explaining method in a busy clinical environment.
Accuracy: To perform inference on structured Electronic Health Records (EHR) data, conventional machine learning methods often collapse records within a predefined time window into summary statistic vectors. While practical, this practice limits the predictive models’ ability to capture temporal information and thereby affects prediction accuracy. Deep learning methods are increasingly acknowledged for their ability to process complex sequential data and could potentially improve the capture of nuanced temporal features in EHRs. However, EHRs are typically stored in relational databases optimized for efficient transactional storage and retrieval, which are not inherently structured for the type of sequential data analysis done by deep learning models. Furthermore, the varied lengths and irregular intervals of EHRs, along with their tendency for long sequences, pose a challenge to common deep learning techniques, which typically assume uniform sequence lengths and intervals. Developing a deep learning framework that can appropriately preprocess these records and extract temporal patterns for accurate prediction remains a challenge and an open problem.
Quality Measures: Effective management of chronic conditions like asthma requires continuous monitoring of care processes and outcomes. At Veterans Affairs (VA) medical centers, existing methods for quality measurement each present their own limitations. Electronic quality measures (eQM), while scalable, employ predefined rule-based algorithms that exclude clinical notes, compromising accuracy. In contrast, the External Peer Review Process (EPRP) serves as a gold standard due to its expert-driven, manual approach. This method involves a thorough chart review process that often includes reading clinical notes and discussion sessions, ensuring high reliability but at the cost of scalability. Machine learning techniques, rapidly advancing and demonstrating impressive results in natural language processing (NLP) tasks, show promise in facilitating the development of an automated system that can integrate eQM’s scalability with EPRP’s accuracy by leveraging clinical notes.

In this research, we aim to tackle these identified challenges to improve the practicality and efficacy of the application of machine learning methods in real-world healthcare scenarios, setting the stage for the following specific aims:
Aim 1: To analyze the errors and the timeliness of the risk warnings given by our machine learning model for predicting asthma hospital encounters at UW Medicine.
Aim 2: To develop a method to rank the rule-based explanations generated for machine learning predictions. The method will be tested on the case of predicting asthma hospital encounters at UW Medicine.
Aim 3: To improve the accuracy of predicting asthma hospital encounters by developing a deep learning framework that leverages temporal information from structured EHR data. The model will be developed and tested on data of Kaiser Permanente Southern California.
Aim 4: To construct a machine learning prototype incorporating NLP techniques to simulate the EPRP quality measure process at VA medical centers.

Title: A mixed-method comparison of social determinant of health (SDOH) documentation between an academic medical center and a community health center 
Student: Carolin Spice
Date/Time: December 1, 2023 / 2:00pm
Location: SLU, Room E130A
Zoom:   https://washington.zoom.us/j/96392187186?pwd=dXNhaEZ0UElmdFVCWTNhUDd0bzh3QT09
Abstract: Efforts to standardize documentation of social determinants of health (SDoH) remain challenging given the variability and flexibility of natural language to describe the determinant.  Most biomedical health informatics research are carried out within academic medical centers (AMC), including natural language process (NLP) for SDoH.  In contrast, very little research has been carried out at community health centers (CHC), which care for a disproportionate share of individuals with high unmet social needs.  This research aims to 1) describe the differences and similarities between two AMC and two CHC clinics in the documentation of SDoH and 2) explore knowledge, behaviors, and practices of providers in the collection and documentation of SDoH within each health system.  The results of this study can serve to inform the design of an approach that can ease the collection and documentation of SDoH.

Title: Generalizable Methods for Clinical Text Classification and Risk Factors Mining with NLP Extracted Symptoms from Clinical Notes
Student: Weipeng Zhou (advised by Meliha Yetisgen)
Date/Time: December 4th (Monday), 8:00 AM
Location: Online only
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 aims 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.

November 6 – November 10, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590
Presenter: Erik Van Eaton, MD FACS
Thursday, November 16th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person
Zoom Information: https://washington.zoom.us/my/bime590
Title:  Machine Learning and Clinical Predictions: Who Listens to the Machine?

Abstract:
A machine learning model can predict impending hypoglycemia in hospitalized patients with an area under the receiver operating curve value of 0.94. This is very good performance but there will still be many false positive predictions and false negative predictions.  The more rare that hypoglycemia becomes, the greater are the chances that an alert is wrong. This worsens the risk that users will ignore the system. Can this predictive tool really be used clinically? Who should its intended user be, and what interaction designs could make it more acceptable to them?

Presenter Bio:
Erik Van Eaton, MD, FACS, is an Associate Professor of Surgery and Surgical Critical Care 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. Dr. Van Eaton is the Chief Innovation Officer for TransformativeMed Inc., a spin-out company from the University of Washington. He helps the company commercialize licensed biomedical informatics technology developed at the University of Washington. Projects underway by Dr. Van Eaton’s research group include: observational trials of an EHR-embedded electronic glycemic management system at multiple health systems in the US, and studies about discharge efficiency, prophylaxis adherence, and physician EHR satisfaction in Saudi Arabia.

PAPERS & PRESENTATIONS
Alipour E, Pooyan A, Shomal Zadeh F, Darbandi AD, Bonaffini PA, Chalian M. Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review. Diagnostics. 2023; 13(21):3372. https://doi.org/10.3390/diagnostics13213372

Steve Ma, Longxuan Fan, Sai Anish Konanki, Eva Liu, John H. Gennari, Lucian P. Smith, Joseph L. Hellerstein, and Herbert M. Sauro. VSCode-Antimony: A Source Editor for Building, Analyzing, and Translating Antimony Models.  Bioinformatics, 2023 (in press).

Woosub Shin, John H Gennari, Joseph L Hellerstein, Herbert M Sauro. An Automated Model Annotation System (AMAS) for SBML Models. Bioinformatics, 2023 (in press).

Chen, A. T., Johnny, S., Chaliparambil, R. K., Wong, S. H., Glass, J. E. (2023). Leveraging insights from social media to develop stigma reduction interventions. Poster presented at Addiction Health Services Research Conference 2023, Oct. 18-20, 2023, New York, NY.

Sharon Wong received the Best Student Paper Award for the paper: Wong, S., Kaneshiro, J., Chen, A. T. (2023). Incorporating stakeholder perspectives when designing a participant insight dashboard for an online community-based health intervention. Presented at the “Exploring Collaborative Interpretive Practice” workshop, co-located with the annual meeting of the Association for Information Science & Technology (ASIS&T), Oct. 27-31, London, UK.

Chen, A. T., Ahmed, F., Chien, S.-Y., Luu, J., Ren, X., Sharma, R. K. (2023). Linguistic and cultural dimensions of community-engaged and collaborative interpretive research in healthcare. Selected as Best Poster Runner-Up at “Exploring Collaborative Interpretive Practice” workshop, co-located with the annual meeting of the Association for Information Science & Technology (ASIS&T), Oct. 27-31, London, UK.

Annie Chen, Melissa Ocepek, and Yan Zhang co-edited an Library and Information Science Research special issue, “Research Methods in Information Behavior Research,” Vol. 44, Issue 4 (Oct. 2022), which received the Special Interest Group (SIG) Publication of the Year award at the Association for Information Science and Technology (ASIS&T) 2023 Annual Meeting, in London, UK, Oct. 31, 2023.

ANNOUNCEMENTS
Heidi Krueger will be retiring from her position as Administrator in the Department of Biomedical Informatics and Medical Education effective December 1st. Heidi has spent over 12 years in her current role and has played a pivotal role during this time partnering with the Department Chair during a time of exponential growth, relocation from Health Sciences to South Lake Union and many key departmental and organizational level initiatives. We are grateful for Heidi’s contributions to the Department and the School of Medicine. Recruitment for the new Administrator will begin shortly. During the recruitment period, Heather Clausnitzer will serve as Interim Administrator for the department. Please join us in wishing Heidi well in her retirement and welcome Heather as Interim Administrator.

Hasan Ahmad, DO, MBA, FACP, has been elected a Fellow of the American College of Physicians (FACP), the society of internists. The distinction recognizes achievements in internal medicine, the specialty of adult medical care.

Annie Chen will serve as Papers Track Chair for annual meeting of the Association for Information Science and Technology (ASIS&T) 2024 to be held in Calgary, Canada, Oct. 25-29, 2024. A preliminary CFP is available here: https://www.asist.org/am24/.

October 30 – November 3, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Andrea Hartzler, PhD
Thursday, November 9th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person
Zoom Information: https://washington.zoom.us/my/bime590

Title:  Using Social Signals to Understand Implicit Bias and Promote Patient-centered Interactions
Abstract: Implicit bias is associated with healthcare disparities, but is often hidden in patient-provider interactions. Nonverbal communication cues that reflect social dimensions of clinical interactions, such as verbal dominance, have been shown to signal provider implicit bias. We set out to understand and address implicit bias with novel technology that makes these social signals visible through the UnBIASED project (Understanding Biased patient-provider Interaction And Supporting Enhanced Discourse). In this session, we will share our work to model social signals through a prospective study to record and analyze primary care visits for relationships among social signals and nonverbal communication, patient- and provider-reported visit quality, and provider implicit bias. Using the Roter Interaction Analysis System (RIAS) as an analytic framework, we examine social signals as a means to automatically identify hidden bias in clinical interactions. Finally, we will share plans to test the implementation of social signals in an automated communication assessment and feedback tool designed to raise clinician awareness of implicit bias and promote patient-centered interactions.
Presenter Bio: Andrea Hartzler is a professor in the department of Biomedical informatics and Medical Education at the University of Washington. She co-directs the Clinical Informatics and Patient-Centered Technologies Graduate program and serves as a clinical informatics leader in operational efforts to promote patient experience at UW Medicine. Dr. Hartzler’s research spans human-computer interaction, consumer health informatics, clinical informatics with a focus on the human-centered design of technologies that promote health equity. She uses participatory approaches and mixed methods that emphasize user experience and integration of informatics solutions into healthcare. She earned a PhD in Biomedical Informatics and was an Assistant Investigator at Kaiser Permanente Washington Health Research Institute before joining the faculty at UW School of Medicine in 2017.

PAPERS & PRESENTATIONS
Xiruo Ding, Zhecheng Sheng, Brian Hur, Feng Chen, Serguei Pakhomov, Trevor Cohen. Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts. Accepted to NeurIPS 2023 Workshop on Distribution Shifts (DistShift): New Frontiers with Foundation Models. New Orleans, 2023.

ANNOUNCEMENTS

The “professional development” course (BIME 585) is hosting an alumni panel for Q&A about careers following a PhD in Biomedical & Health Informatics. Each will provide a brief presentation about their career path, and the value (or Not!) of a PhD in BHI, and then we’ll have Q&A from all students.
This year we have the following fine folk (with year of BHI graduation):

  • Lucy Lu Wang (2019), Assistant Professor, UW iSchool
  • Chethan Jujjaarapu (2021), Senior Data Scientist, Prealize Health
  • William Kearn (2023), Staff ML Engineer, Headspace, Inc.
  • Ross Lordon (2019), Design Researcher, Microsoft
  • Kathleen Ferrar(2022), Clinical Informatics Analyst, Mt Sinai  Institute for Genomic Health
  • Hannah Burkhardt(2022), Senior Software Engineer, Truveta

This panel will be in room T-359 of the lovely Health Sciences Building on Wednesday, Nov 8th at 10:30 am. All are welcome to attend. This will be an in-person only event.

Two BHI students, Serena Jinchen Xie and Xiruo Ding, received $10,000 awards from the Azure Cloud Computing Credits Program through the UW eScience Institute to support their dissertation work. More information below:

  • Title: Enhancing Empathy and Reducing Bias in LLM-Powered Support for Family Caregivers

Investigators: Serena Jinchen Xie, Trevor Cohen, & Weichao Yuwen

  • Title: Deconfounding Deep Transformer Networks for Clinical NLP

Investigators: Xiruo Ding and Trevor Cohen
_____________________________________________________________________________________
Webinar: Digital Exposure Notification Systems and Future of Pandemic Response
Friday Nov 3, 10 am-1 pm
As part of the Consortium of Universities in Global Health (CUGH)’s Virtual Global Health Week, Mohsen Malekinejad (UCSF) and Bill Lober are leading a session, with presenters from Washington DC, Nevada, California, Washington State, Switzerland, the Netherlands, and group discussion about the future of smartphone technologies in a pandemic era.
Registration is free and open to all.
https://www.cugh2024.org/virtual-global-health-week (scroll down to Nov 3 1pm-4pm EDT)

October 23 – October 27, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Jimmy Phuong, MSPH, PhD
Thursday, November 2nd – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person

Zoom Information: https://washington.zoom.us/my/bime590

Title:  Research Data Sharing and Secondary-use Data Research with Social Determinants of Health in National Research Consortia

Abstract: Health systems are uniquely positioned to survey the health of their patient population, including the effects of natural hazards, disaster disruptions, and public health emergencies. Health systems are also an integral part of biomedical research consortia, where data sharing supports data-driven research and iterative quality improvements to the data from each health system. Apart from clinical outcomes, health systems are gradually increasing their focus upon collecting and addressing gaps in understanding Social Determinants of Health (or Social Drivers of Health, SDoH) and their dynamic role in maintaining health and wellness. This includes integrating patient-level information as well as place-based information integrated from geocoding and secondary use of spatial-temporal datasets.In this talk, I will discuss the nexus of environmental health, disaster management and population health research, and biomedical informatics research. I will also discuss the directions from health system preparedness, the broader implications towards research data sharing and research consortia with a precision medicine focus, and the analytical capacities needed for research with multiple data types in cloud infrastructure.

Presenter Bio: Dr. Jimmy Phuong is an Acting Assistant Professor in University of Washington (UW) Biomedical Informatics and Medical Education (BIME). He also serves as Lead Research Data Scientist UW Medicine Research IT and Harborview Injury Prevention Research Center and has been a co-champion for the NIH National COVID-19 Cohort Collaborative (N3C) Social Determinants of Health (SDoH) Domain team. Dr. Phuong is a UW BIME PhD alumni of Dr. Sean Mooney’s lab. Prior to joining University of Washington, Dr. Phuong has been a bioinformatics analyst and research fellow at the US Environmental Protection Agency (US EPA) National Center for Computational Toxicology. Dr. Phuong currently focuses on integrating clinical and spatial-temporal data types to support data engineering and research data science applications. His research currently touches on the secondary use and integration of electronic health records, disaster preparedness and injury prevention research, Social Determinants of Health, including research consortial data engineering to advance areas of population health and precision medicine research.

October 16 – October 20, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590
Presenter: Nic Dobbins, PhD
Thursday, October 26th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person
Zoom Information: https://washington.zoom.us/my/bime590

Title:  Enabling Research with NLP-driven Cohort Discovery and Query Generation

Abstract:
Determining patients eligible for randomized controlled trials and other biomedical research studies is a frequent challenge. Many studies do so by either manual chart review or with the assistance of technical personnel using databases of electronic health record data. In this talk, I will discuss several projects aiming to save time and costs by enabling self-service applications for determining patients meeting user-defined criteria. The first, Leaf, uses a novel drag-and-drop abstract syntax tree-based approach to generate database queries. Next, I’ll discuss LeafAI, a natural language interface application allowing users to type criteria in free-text, which are then transformed into queries using a hybrid large language model (LLM) and rule-based approach. I’ll additionally discuss LeafAI’s chat-like user interface and functionality for multi-hop biomedical reasoning using a knowledge base. I’ll conclude with a discussion of ongoing and future projects on LLM-driven semi-autonomous agents and other exciting directions.

Presenter Bio:
Nic Dobbins received his BA from the University of Minnesota with a double-major in History and Japanese language. After working abroad in Japan for several years, he returned to the US for his Master’s degree in Library & Information Science from the UW iSchool. Nic’s PhD in biomedical informatics from the University of Washington explored the intersections of cohort discovery, dynamic database query generation, question answering, data discovery, human-computer interaction and natural language processing (NLP). Nic is the creator of Leaf, an open-source cohort discovery application used at academic medical centers and commercial companies around the world. Nic currently works full time as Principal Solutions Architect in UW Medicine Research IT. His current research focuses on cohort discovery and semi-autonomous agents driven by large language models (LLMs).

PUBLICATIONS & PRESENTATIONS

Janice Sabin was invited to St. Jude Children’s Research Hospital, Memphis TN, to conduct continuing education on implicit bias in healthcare as part of the St. Jude Clinical Faculty Development Workshop Series, October 10, 2023. Janice conducted two sessions, a systems-based practice seminar with clinical fellows and a clinical faculty development workshop. The St. Jude workshop series is designed to provide support for faculty as they educate, supervise, and mentor a wide variety of medical trainees, including clinical fellows, residents, and medical students.

Weipeng Zhou, Laura C Prater, Evan V Goldstein, Stephen J Mooney, Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach, JMIR Ment Health 2023;10:e49359, doi: 10.2196/49359 PMID: 37847549

October 9 – October 13, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590

Presenter: Jeff Leek, PhD
Thursday, October 19th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present in-person
Zoom Information: https://washington.zoom.us/my/bime590

Title: Developing a National Scale Gene Expression Resource
Abstract: The recount project is a joint effort of my lab and the labs of Kasper Hansen, Ben Langmead, Andrew Jaffe, Abhi Nellore and Leo Collado Torres to collect, process, and normalize all published RNA-seq measurements from human and mouse. To build this resource we had to address key computational, infrastructure, statistical, machine learning, data governance, and data integration challenges posed by distributed data collection. The recount project now comprises more than 300,000 samples and has enabled projects across hundreds of labs. I will describe the development of the recount project and identify analogies to building the data infrastructure for precision oncology at Fred Hutchinson Cancer Center.

Presenter Bio: Jeff is Chief Data Officer, Vice President, and J Orin Edson Foundation Chair of Biostatistics at the Fred Hutchinson Cancer Center. Previously, he was a professor of Biostatistics and Oncology at the Johns Hopkins Bloomberg School of Public Health and co-director of the Johns Hopkins Data Science Lab. His group develops statistical methods, software, data resources, and data analyses that help people make sense of massive-scale genomic and biomedical data. As the co-director of the Johns Hopkins Data Science Lab he helped to develop massive online open programs that have enrolled more than 8 million individuals and partnered with community-based non-profits to use data science education for economic and public health development. He is a Fellow of the American Statistical Association and a recipient of the Mortimer Spiegelman Award and Committee of Presidents of Statistical Societies Presidential Award.

PUBLICATIONS & PRESENTATIONS

Krista Alexandria Marie Bond, Javier Rasero Daparte, Raghav Madan, Jyotika Bahuguna, Jonathan E Rubin, Timothy Verstynen (2023). Competing neural representations of choice shape evidence accumulation in humans. eLife 12:e85223 https://doi.org/10.7554/eLife.85223

Lees AF, Beni C, Lee A, Wedgeworth P, Dzara K, Joyner B, Tarczy-Hornoch P, Leu MG. Uses of Electronic Health Record Data to measure the Clinical Learning Environment of Graduate Medical Education Trainees:  A Systematic Review. Accepted. Academic Medicine.  DOI: 10.1097/ACM.0000000000005288.

Pozdeyev N, Dighe M, Barrio M, Raeburn C, Smith H, Fisher M, Chavan S, Raaels N, Shortt J, Lin M, Leu MG, Clark T, Marshall C, Haugen BR, Subramanian D, Regeneron, Crooks K, Gignoux C, Cohen T.  Thyroid cancer polygenic risk score combined with deep learning analysis of ultrasound images improves the classification of thyroid nodules as benign or malignant.  Accepted.  J Clin Endocrinol Metab.

Stoffel M, Leu MG, Barry D, Deam N, Heissenbuttel A, Hrachovec J, Huq Saifee N, Migita DS, O’Hare MP, Villavicencio C, Delaney M.  Improving Electronic Blood Ordering and Supporting Administration Workflows Significantly Reduces Blood Wastage.  Accepted. Transfusion.

An article Mike Leu worked on was recently selected as one of 7 landmark articles in Pediatric Informatics published in Pediatrics in the past 75 years:  Spooner SA, COCIT.  Special Requirements of Electronic Health Record Systems in Pediatrics.  Pediatrics.  2007; 119: 631-637; PMID 17332220.

October 2– October 6, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Rich Green, PhD

Thursday, October 12th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present via Zoom only
Zoom Information: https://washington.zoom.us/my/bime590

Title: Trends in Personalized Medicine: Translational Insights from Mouse Models to Clinical Diagnostics

Abstract: The era of personalized medicine promises bespoke therapeutic interventions, tailored to an individual’s genetic makeup. This lecture will delve into the use of mouse models, specifically focusing on the Collaborative Cross, to understand the genetic and environmental nuances of diverse diseases. The Collaborative Cross offers a powerful resource, encompassing a broad genetic diversity, making it an ideal tool for dissecting complex traits and genetic interactions, thereby paving the way for targeted therapies.
Transitioning from animal models, we will explore the cutting-edge technological advancements made by Natera. Natera’s suite of technologies, including the notable RenalSight, harnesses the power of genetic testing and big data analytics. RenalSight, in particular, stands out as a beacon in kidney disease prediction, identifying individuals at risk of developing chronic kidney disease, facilitating early intervention, and personalized treatment strategies.
In combining insights from both mouse models and human-centric technologies like those of Natera, this lecture aims to paint a holistic picture of the current landscape and future potential of personalized medicine. Attendees will leave with a deep appreciation for the confluence of basic research and technological innovation, driving the future of healthcare towards more precise and individualized solutions.

Presenter Bio: Richard (Rich) Green is a Senior Bioinformatics Scientist in the data insights team.  Prior to joining Natera, Rich severed as a Research Consultant in the department of Medical Genetics at the University of Washington and as a Senior Bioinformatics Scientist at the Allen Institute for Immunology. His previous work focused on applying computational methods to biological and clinical problems. His main research interests are computational biology, genetics, and immunology. He received his Master’s in Bioinformatics from Northeastern University and his PhD in the department of Biomedical Informatics and Medical Education (BIME) from the University of Washington in Seattle.

PUBLICATIONS & PRESENTATIONS
Bedmutha MS, Bhat A, Mangal S, Bascom E, Pratt W, Wood B, Sabin J, Weibel, N, Hartzler AL. Towards inferring implicit bias in clinical interactions using social signals. AMIA AI Showcase, AMIA Annual Symposium 2023.

Kashyap N, Bedmutha MS, Chaudhary P, Wood B, Pratt W, Sabin J, Hartzler AL, Weibel, N.  Towards Enhanced Human Activity Recognition through Natural Language Generation and Pose Estimation. Generative AI for Pervasive Computing (GenAI4PC) Symposium, Ubicomp 2023.

UPCOMING GENERAL EXAM
Title: How to support people living with cystic fibrosis to incorporate innovative practices and treatments into their lives.
Student: Nick Reid
Date/Time: Thursday, October 12th 2023, at 8am PT
Location: SLU (Room C123A) and https://washington.zoom.us/my/andreahartzler

Abstract: Care innovations — meaning new practices and treatments — are changing how people living with cystic fibrosis (CF) live their lives. Over the last decade, the predicted life expectancy of a person living with CF has almost doubled from 36 to 65 years old, and is expected to continue rising. CF is a rare genetic disease, traditionally associated with childhood death due to progressive lung failure, requiring burdensome care routines from people living with CF and their caregivers. Many care innovations have contributed to the improvements in CF care — particularly the medication Trikafta, that can dramatically improve CF lung function, and for some reducing daily CF care routines to taking a pill twice daily. Yet, medical guidelines and knowledge about Trikafta’s efficacy are still emerging and more care innovations promise to continue changing the lives of people living with CF — and people living with CF already struggle to incorporate current CF care practices and treatments into their lives. Inductive qualitative research is needed to understand how to support people living with CF to incorporate care innovations into their lives.
Aim 1: To describe how people living with CF have incorporated care innovations, I will interview people living with CF about critical incidents related to learning about and adopting care innovations to construct a grounded theory of CF care innovation incorporation.
Aim 2: To identify preferred methods of CF care innovation incorporation, I’ll survey a sample of people living with CF about how they would prefer to learn about or adopt different care innovations.
Aim 3: To design recommendations for how to support people living with CF incorporate care innovations, I will conduct design workshops in an asynchronous remote community of people living with CF using reflective and scenario-based design activities and nominal group technique. By conducting this research, I’ll understand how to support people living with CF to incorporate care innovations, which may be transferable to other communities living with rare diseases.

ANNOUNCEMENTS
Savitha Sangameswaran (PhD, 2023) was selected as selected as one of the eight student paper finalists for the upcoming AMIA 2023 Annual Symposium for her paper titled “Meditation for me is just an app in my phone”– co-designing mind-body technologies for sleep with adolescents’. Congratulations Savitha and best of luck in this year’s competition!

September 25 – September 29, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590: speaker will be in-person
Presenter: Peter Tarczy-Hornoch, MD, FACMI
Thursday, October 5th – 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.

ANNOUNCEMENTS
Melissa Clarkson has received funding from NIGMS to develop a derivative of the Foundational Model of Anatomy (FMA) ontology. The FMA was developed under the leadership of Cornelius Rosse and Jim Brinkley over a 20-year period. The new ontology will be called the Foundational Model of Human Anatomy (FMHA) and will provide anatomical information in a form designed to be used by the next generation of intelligent systems. As part of this project her team will also create libraries of anatomical graphics that will serve as both visual standards and components of user interfaces. Dr. Clarkson is an Assistant Professor at the University of Kentucky and alumni of the UW BHI PhD program, with dissertation work completed in the Structural Informatics Group led by Jim Brinkley.

NEWS FROM ALUMNI
Two updates from Hyunggu Jung (BHI Ph.D., 2017):

    • He was promoted to associate professor of Computer Science & Engineering in September 2022. He is now an associate professor in the Department of Computer Science & Engineering and the Department of Artificial Intelligence at the University of Seoul. Personal website: http://hyunggujung.com/
    • He directs a research group, Human-Centered Artificial Intelligence Lab (HCAIL). HCAIL pursues research in the combinations of the following directions: artificial intelligence, health informatics, and human-computer interaction. HCAIL aims to advance AI research through design and engineering to support individuals with special needs (e.g., older adults with chronic diseases, content creators with visual impairments, and model developers) across multiple domains: health, social media, and education. HCAIL website: https://hcail.uos.ac.kr/

He is interested in potential collaborations with BIME faculty, students and alumni – if interested email him at hjung@uos.ac.kr.

September 18 – September 22, 2023

UPCOMING LECTURES AND SEMINARS
BIME 590: Please join us – speaker will be in-person
Presenter: Eric Horvitz, MD PhD
Thursday, September 28th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Title: Large Language Models in Healthcare: Explorations, Challenges, and Directions

Abstract:
Language models with powers of generation are newcomers to the computing toolkit of bioinformatics research. Excitement about possibilities has been tempered with questions about capabilities and limitations, including concerns with accuracy and reliability. I will share reflections and examples drawn from early explorations of the power of generative language models for applications in healthcare including uses of the technology in administrative tasks, education, clinical support, and research.

Presenter Bio:
Eric Horvitz serves as Microsoft’s Chief Scientific Officer. He spearheads company-wide initiatives, navigating opportunities and challenges at the confluence of scientific frontiers, technology, and society, including strategic efforts in AI. His efforts and contributions in bioinformatics include leveraging probability and decision theory for diagnosis and decision support and uses of supervised machine learning to address key challenges in clinical care. He has been elected fellow of the American College of Medical Informatics (ACMI), National Academy of Engineering, Association of Computing Machinery, Association for the Advancement of AI (AAAI), and the American Academy of Arts and Sciences. He serves on the President’s Council of Advisors on Science and Technology (PCAST). He has served on the Board of Regents of the National Library of Medicine, and on advisory committees for the U.S. National Academies of Sciences, Engineering, and Medicine, and National Science Foundation. He earned MD and PhD degrees at Stanford University.  More information and publications can be found at https://erichorvitz.com.

PUBLICATIONS & PRESENTATIONS
E. Alipour, M. Chalian, A. Pooyan, A. Azhideh, F. Shomal Zadeh, and H. Jahanian, “Automatic MRI–based rotator cuff muscle segmentation using U-Nets,” Skeletal Radiology, pp. 1–9, 2023.

Weipeng Zhou, Meliha Yetisgen, Majid Afshar, Yanjun Gao, Guergana Savova, Timothy A Miller, Improving model transferability for clinical note section classification models using continued pretraining, Journal of the American Medical Informatics Association, 2023, Online Preview.

UPCOMING GENERAL EXAM
Title: How to support people living with cystic fibrosis to incorporate innovative practices and treatments into their lives.
Student: Nick Reid
Date/Time: Thursday, October 12th 2023, at 8am PT
Location: SLU (Room C123A) and https://washington.zoom.us/my/andreahartzler

Abstract: Care innovations — meaning new practices and treatments — are changing how people living with cystic fibrosis (CF) live their lives. Over the last decade, the predicted life expectancy of a person living with CF has almost doubled from 36 to 65 years old, and is expected to continue rising. CF is a rare genetic disease, traditionally associated with childhood death due to progressive lung failure, requiring burdensome care routines from people living with CF and their caregivers. Many care innovations have contributed to the improvements in CF care — particularly the medication Trikafta, that can dramatically improve CF lung function, and for some reducing daily CF care routines to taking a pill twice daily. Yet, medical guidelines and knowledge about Trikafta’s efficacy are still emerging and more care innovations promise to continue changing the lives of people living with CF — and people living with CF already struggle to incorporate current CF care practices and treatments into their lives. Inductive qualitative research is needed to understand how to support people living with CF to incorporate care innovations into their lives.
Aim 1: To describe how people living with CF have incorporated care innovations, I will interview people living with CF about critical incidents related to learning about and adopting care innovations to construct a grounded theory of CF care innovation incorporation.
Aim 2: To identify preferred methods of CF care innovation incorporation, I’ll survey a sample of people living with CF about how they would prefer to learn about or adopt different care innovations.
Aim 3: To design recommendations for how to support people living with CF incorporate care innovations, I will conduct design workshops in an asynchronous remote community of people living with CF using reflective and scenario-based design activities and nominal group technique. By conducting this research, I’ll understand how to support people living with CF to incorporate care innovations, which may be transferable to other communities living with rare diseases.

September 11 – September 14, 2023

UPCOMING LECTURES AND SEMINARS

BIME 590: See you soon!

PUBLICATIONS & PRESENTATIONS

Nikita Pozdeyev, Manjiri Dighe, Martin Barrio, Christopher Raeburn, Harry Smith, Matthew Fisher, Sameer Chavan, Nicholas Rafaels, Jonathan A Shortt, Meng Lin, Michael G Leu, Toshimasa Clark, Carrie Marshall, Bryan R Haugen, Devika Subramanian, Kristy Crooks, Christopher Gignoux, Trevor Cohen, Thyroid cancer polygenic risk score improves classification of thyroid nodules as benign or malignant., The Journal of Clinical Endocrinology & Metabolism, 2023; dgad530, https://doi.org/10.1210/clinem/dgad530.

Molly C. Reid, John E. Mittler, James T. Murphy, Sarah E. Stansfield, Steven M. Goodreau, Neil Abernethy, Joshua T. Herbeck. Evolution of HIV virulence in response to disease-modifying vaccines: A modeling study, Vaccine, 2023. https://www.sciencedirect.com/science/article/abs/pii/S0264410X23010277

UPCOMING GENERAL EXAM
Title: Deciphering neurodevelopmental origins of pediatric brain cancer using single cell genomics and NLP approaches
Student: Ashmitha Rajendran
Date/Time: Thursday, September 21, 2023, 8:30-9:30 am PST (public presentation)
Location: Only zoom
Zoom:    https://washington.zoom.us/j/2066162813
Abstract: A growing body of evidence has shown that many pediatric brain tumors have embryonic origins with driving aberrations arising in precursor or stem cells associated with neurodevelopment.  Several pediatric brain cancers show spatio-temporal and spatio-molecular patterns that map to developmental dynamics. These linked patterns, however, are not fully understood yet. Here, we integrate and expand upon several of the largest single cell atlases of the human prenatal brain and across several pediatric brain cancers to identify developmental transcriptional programs and cellular origins of tumor initiation. We focus on three poorly characterized pediatric brain cancer types with the worst patient outcomes: Diffuse intrinsic pontine gliomas (DIPG), atypical teratoid rhabdoid tumor (ATRT), and medulloblastoma (MB). We hypothesize that expanding on existing neurodevelopmental and pediatric atlases using single cell rna sequencing integrative approaches will allow us to identify the developmental genetic programs involved in pediatric brain cancer progression. In this work, we use both conventional and novel approaches for single cell RNA sequencing analysis—proposing new methods for cell identification and gene module creation rooted in probabilistic topic models and information theory. This work will identify gene sets and cellular lineages putatively linked to the developmental origins of these tumors. It will also investigate tumor-specific transcriptomic dynamics. These analyses are anticipated to ultimately inform targeted therapeutic interventions.

August 28 – Sept 1, 2023

UPCOMING GENERAL EXAM

Title: NeuroPathPredict (NPP), a novel data-driven approach to map AD brain pathology distribution
Student: Raghav Madan
Date/Time: Friday, September 8, 2023, 12:30-1:30 pm PST (public presentation)
Location: 750 Republican Street, Building E, Room E103
Zoom:   https://washington.zoom.us/my/jhgennari?pwd=TUx0clkwKzdnS1ZQV1dXRnZqMWMzZz09

Abstract: Alzheimer’s disease (AD) currently afflicts over 6 million Americans and millions more globally. Several neuropathological protein aggregates like amyloid-beta plaques (Aβ), neuro-fibrillary tau tangles (NFT), and transactive response DNA-binding protein of 43 kDa (TDP-43) have been linked with AD. Existing methods to measure neuropathology (NP) distribution, including targeted in-vivo PET imaging and post-mortem histopathology have drawbacks. PET imaging can only measure one pathology at a time, and is limited by off-target tracer binding. Histopathology provides precise measurements for multiple pathologies but for only a handful of regions. A whole-brain histopathological analysis is theoretically possible, but prohibitively resource-intensive. Precise brain-wide spatial NP distribution can bolster AD research and may help develop specific interventions. For my dissertation, I propose NeuroPathPredict (NPP), a novel data-driven computational approach to precisely map brain-wide NP distribution. NPP will use existing post-mortem quantitative neuropathology data from a few regions. It will adapt techniques from air pollution spatial modeling research. Further, NPP will be built atop an Integrated-Brain Information System (I-BIS), a human brain connectome information- schema. Specific aims to develop NPP are as follows:
Aim 1: To establish a foundational framework for the prediction of the spatial distribution of neuro-fibrillary tau tangles (NFTs) at a regional level.
Aim 2: To extend the framework for the prediction of the spatial distribution of neuro-fibrillary tau tangles (NFTs) at a voxel level.
Aim 3: To implement the framework for the prediction of the spatial distribution of Aβ, and TDP-43 at a voxel level.