News and Events
Chair’s Message
We 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
March 11 – March 15, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590 – On break until March 28th!
PAPERS & PRESENTATIONS
Yujuan Fu*, Giridhar Kaushik Ramachandran*, Nicholas J Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, ̈Özlem Uzuner, and Meliha Yetisgen. Extracting social determinants of health from pediatric patient notes using large language models: Novel corpus and methods. Accepted by LREC-COLING, 2024.
Wen-wai Yim, Yujuan Fu, Asma Ben Abacha, and Meliha Yetisgen. To err is human, how about medical large language models? comparing pre-trained language models for medical assessment errors and reliability. Accepted by LREC-COLING, 2024
Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Özlem Uzuner, Martin Gunn and Meliha Yetisgen. A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models. Accepted for 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).
March 4 – March 8, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590 – On break until March 28th!
PAPERS & PRESENTATIONS
Zeng, N.C. Jani, A.M. Sotolongo, G. Luo, M. Arjomandi, M.J. Falvo. Pulmonary function, chronic respiratory symptoms, and functional limitation among Veterans in the Airborne Hazards and Open Burn Pit Registry. American Thoracic Society International Conference, San Diego, CA, May 2024.
GENERAL EXAM
Title: Use of the Electronic Health Records to facilitate phenotyping, comorbidity analysis, and genomics
Student: Su Xian
Date/Time: 3:30 pm, March 7th, 2024 – TODAY!
Location – Zoom only: http://bit.ly/seanmooneyzoom
Abstract: One of the contemporary medical research challenges is EHR-based digital phenotyping. The electronic MEdical Records and Genomics (eMERGE) consortium has launched Phenotype KnowledgeBase (PheKB), an EHR-based Phenotyping knowledge base engaging multiple sites of large hospitals and universities to share their phenotyping algorithms developed using the EHR data. Though most of the algorithms stored in PheKB are rule-based and validated by domain expertise, there are efforts to develop machine learning algorithms for EHR-based phenotyping. Various types of machine learning and deep learning methods have been tested to derive EHR-based phenotyping algorithms, including Support Vector Machines (SVM), random forest, logistic regressions, and neural network architectures. In this work, we will: 1) Explore the potential of using EHR data to phenotype patients. We will develop an unsupervised machine learning algorithm, starting from learning the diagnosis codes and procedure codes, to finally learning numerical embeddings for each patient, using the data from the eMERGE consortium. 2) Evaluating the performance of the algorithm on disease prediction and bulk phenotyping. Applying the designed algorithm to study the comorbidities of phenotypes, revealing heterogeneity using a few selected phenotypes as cases, including colorectal cancer, and systemic lupus erythematosus, to facilitate personalized treatment and gain an understanding of the complicated disease. 3) Using a rule-based phenotyping algorithm derived from the eMERGE consortium, we focus on a complicated psychological disease – depression – using EHR-based phenotyping algorithms to study the genetic risk factors and uncover the molecular mechanisms of depression.
ANNOUNCEMENTS
Nic Dobbins, PhD has accepted a position as an Assistant Professor in the Johns Hopkins Dept. of Medicine, in the Biomedical Informatics & Data Science section, starting 4/1. Congrats Nic!
From Savitha Sangameswaran: Invitation to Participate in Research Study of Information in Translational Research Teams
Are you a clinical and translational researcher? You are invited to participate in a study on how funded translational research teams manage information. This study Information Management Prototype for Clinical and Translational Research (IMPACT-CTR), is funded by an RO1 from National Library of Medicine and aims to understand the tools and strategies teams use in seeking, using, creating, sharing, storing, and retrieving information while conducting collaborative clinical and translational research. We will use what we learn from the study to create training materials to help teams develop evidence-based information strategies that can make CTR more efficient and effective.
Please visit https://impactctr.sagebionetworks.org/ for more information about this study and how you can participate!
To participate in this study, please complete this interest form and a member of the research team will reach out to schedule a brief informational call!
February 26 – March 1, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
Diane M. Korngiebel, DPhil, MA
Thursday, March 7th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Title: Anthropomorphism: What is it and why does it matter for Artificial Intelligence applications?
Abstract: Dr. Korngiebel will present on the importance of considering anthropomorphism in the context of generative AI applications that leverage Large Language Models (LLMs) and Large Multi-model Models (LMMs). The presentation will start by defining key terms, discuss anthropomorphism and how it relates to generative AI, and suggest areas for further research into both design and deployment that take into account the challenges and opportunities that accompany anthropomorphism.
Speaker Bio: Diane M. Korngiebel is currently a Senior AI Ethicist in Trust & Safety at Google and has been an ELSI (ethical, legal, and social implications) Scholar at Google since Oct. 2021. Before joining Google, Dr. Korngiebel spent a year at The Hastings Center as a Research Scholar, and until 2020, she was an Associate Professor in the Department of Biomedical Informatics and Medical Education and an adjunct Associate Professor in the Department of Bioethics and Humanities at the University of Washington School of Medicine in Seattle; she maintains affiliate faculty status in both UW departments. Her current interests focus on the ethics of using generative AI in healthcare and wellness support.
Dr. Korngiebel’s work has appeared in the American Journal of Public Health, Nature: Genetics in Medicine, NPJ Digital Medicine, and PLoS Genetics. Before joining Google, Dr. Korngiebel was the Principal Investigator on three NIH ELSI program grants. She is the chair of the AMIA ELSI Working Group, serves as an IEEE working group member developing recommendations for organizations regarding AI governance, and has helped inform ISO standards for health-related smartphone apps.
Medical Data Science Seminar – NEXT Tuesday, March 5th at 1pm PT – Zoom link
Creating a unique Acute Myeloid Leukemia (AML) Digital Twin for cancer patients contributes to innovative treatments
Ilya Shmulevich, PhD
Professor, Institute for Systems Biology & Affiliate Professor
Departments of Bioengineering and Electrical Engineering, UW
PAPERS & 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 and Metabolism. 2024 Jan;109(2):402-412. DOI: 10.1210/clinem/dgad530. PMID: 37683082.
Link to featured article: https://academic.oup.com/jcem
GENERAL EXAM
Title: Operationalizing Predictive Decision Support and Disease Phenotyping to Improve Healthcare Outcomes in Chronic Obstructive Pulmonary Disease
Student: Siyang (Sunny) Zeng
Time: 7:00 am, March 1st, 2024
Location: Zoom only – https://washington.zoom.us/j/4666998448?pwd=dXo1NjFCQkNJclFYc2Y0SHN3c0JPZz09
Meeting ID: 466 699 8448, Password: 524369)
Abstract:
Chronic obstructive pulmonary disease (COPD) is a major cause of death and places a heavy burden on healthcare. Severe COPD exacerbations requiring emergency department visits or inpatient stays often cause irreversible decline in lung function and health status and account for majority of the total medical cost related to COPD. Successful preventive strategies such as care management can reduce severe exacerbations in patients with COPD, however, due to limited resources and service capacity, only a small portion of patients could enter a care management program. Thus, its effectiveness is upper bounded by how accurately it enrolls patients who are at risk for severe exacerbations. There is a need to facilitate accurate pinpointing of high-risk patients in order to optimize resource allocation and patient outcomes.
Although predictive models can identify high-risk patients for preventive care, there are several obstacles to operationalize the predictive decision support and improve outcomes in practice: (1) existing predictive models for severe COPD exacerbations are inaccurate and suboptimal for clinical use; (2) predictive models lack explanability and interpretability, forming barriers for making sense of the prediction or identifying the most effective preventive strategies; (3) despite being built with the purpose of forewarning to prevent future exacerbations, existing predictive models were seldom evaluated for their impact on actual clinical actions; and (4) incomplete linkages between disease mechanisms and clinical outcomes hinders the effect of therapies or management strategies.
In this research, we aim to tackle these obstacles by (1) developing the most accurate model to predict patients at high-risk for future severe COPD exacerbations, (2) providing explanations with clinical recommendations for the high-risk prediction, (3) piloting a user study to evaluate the impact of providing explanations with clinical recommendations for a high-risk prediction on care management enrollment decisions, and (4) determining lung function phenotypes associated with COPD mechanisms and clinical outcomes.
February 19 – February 23, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
Sarah Biber, PhD
Thursday, February 29th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Title: A Next Generation Multimodal Data Platform for Discovery and Translation in Alzheimer’s
Abstract: Alzheimer’s is a complex and devastating disease. The advent of breakthrough disease modifying therapeutics like Leqembi have created new hope for a future where Alzheimer’s disease can be detected and treated before cognitive symptoms occur. However, developing effective early detection tools, gaining a comprehensive understanding of disease drivers and mitigators, and developing precision therapeutics requires a holistic understanding of the disease. This ambitious task demands data driven approaches and leveraging disparate modalities of research and clinical data that are harmonized and shareable. The National Alzheimer’s Coordinating Center has built a next generation multimodal data integration and harmonization platform that will enable researchers to ask and answer the most pressing questions in the field and gain critical new insights into Alzheimer’s.
Presenter Bio: Sarah Biber, PhD, is the Executive Director of the National Alzheimer’s Coordinating Center (NACC), based at the University of Washington. She co-leads NACC’s scientific and strategic direction and $58M project portfolio, and functions as a co-PI with NACC’s PI and Director, Dr. Walter Kukull. Within this role, Dr. Biber represents NACC with national and international partners, spearheads national initiatives, leads development of major grant applications, leads academic and industry partnerships, and oversees NACC’s tech, operations, research, communications, and grants and finance teams. Under her leadership, NACC has undergone a massive informatics transformation to establish a next generation multimodal data platform for discovery and translation in Alzheimer’s Disease and Related Dementias (ADRD).
PAPERS & PRESENTATIONS
Payne, Thomas. I am not burned out. This is how I write notes. Accepted abstract, AMIA Clinical Informatics Conference, May 2024.
February 12 – February 16, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
David Crosslin – more information coming next week
Thursday, February 22nd – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
PAPERS & PRESENTATIONS
Kim KK, Backonja U. Perspectives of community-based organizations on digital health equity interventions: a key informant interview study. J Am Med Inform Assoc. 2024 [ePub ahead of print]. doi:10.1093/jamia/ocae020
Andrea Hartzler shared about a recent publication on the effectiveness of the UW Medicine vaccine equity outreach effort in the Journal of Applied Clinical Informatics!
Xie S, Mah N, Chew L, Ruud J, Hernandez J, Lowery J, Hartzler AL. Improving Vaccine Equity: How Community Engagement and Informatics Facilitate Health System Outreach to Underrepresented Groups Appl Clin Inform 2024; 15(01): 129-144.
This paper reports on the positive impact of COVID-19 vaccine equity outreach strategies deployed at UW Medicine in 2021 to reach underrepresented and vulnerable groups. Strong community partnerships were a key ingredient of success!
ANNOUNCEMENTS
Applications for the 2024 HIPRC Dr. Frederick P. Rivara Endowment Award are now being accepted. Graduate students and post docs are eligible. https://hiprc.org/blog/2024-hiprc-rivara-endowment-injury-research-award/
Sean Mooney appointed to direct the NIH CIT
Sean Mooney, Ph.D., will be leaving the BIME department and UW to direct the NIH Center for Information Technology, one of the 27 Institutes and Centers (ICs) of the NIH. Sean has been at the University of Washington for just over nine years. We are grateful for all the contributions that Sean has made during his years at the University of Washington and congratulate him on this fantastic opportunity for both him and for NIH.
January 29 – February 2, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
More information coming next week
Thursday, February 8th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
Medical Data Science Seminar
Dr. Patrick Boyle, Assistant Professor, Dept. of Bioengineering, UW
February 6th at 1pm PT
Zoom link
Dr. Boyle leads the Cardiac Systems Simulation (CardSS) Lab where they use computer models of the heart to cultivate new knowledge about heart rhythm disorders. Using MRI scans from individual patients to power these complex “virtual heart” simulations, they are able to discover underlying causes of disease or devise new treatment strategies that deliver better health outcomes for patients and communities.
PAPERS & PRESENTATIONS
Dong, G. Luo. Progress Estimation for End-to-End Training of Deep Learning Models with Online Data Preprocessing. IEEE Access, 2024
GENERAL EXAM
Title: SSI Identification Across Multiple Healthcare Facilities and Surgery Types Using Multimodal Data and Deep Learning
Student: Arjun Chakraborty
Date/Time: 2/6/2024, 3 PM
Location: Building C, 850 Republican Street, Room #C122
Zoom: https://washington.zoom.us/my/peter.th
Abstract:
Surgical Site Infections (SSI) can lead to serious complications such as cellulitis and sepsis, increase the risk of death after surgery, and incur a cost of approximately $10 billion dollars annually in the United States. To reduce the incidence of SSI, surveillance is crucial. Currently, manual methods that involve chart review with or without wound inspection are utilized. However, these methods are both time-consuming and expensive. To overcome this issue, automated surveillance approaches have been proposed. These approaches include extracting information from structured electronic health record (EHR) data using various feature engineering methods or extracting information from clinical text data in the EHR through various natural language processing (NLP) methods to predict whether a surgical case led to an SSI. While these approaches have the potential to address some of the challenges of manual surveillance, further improvements in performance are needed to achieve a substantial reduction in the time incurred by, the cost involved in, and the bias introduced by manual surveillance. In this study, we propose to employ an EHR data driven deep learning model to create an automated surveillance system that achieves better performance and domain adaptation capability compared to existing automated surveillance methods. Our aims include:
Aim 1: Develop and assess an automated SSI surveillance approach using deep learning and multimodal data.
Aim 2: Assess the impact of adding temporal data on the performance of deep learning models which predict SSI.
Aim 3: Assess the domain adaptation capability of our SSI surveillance approach.
ANNOUNCEMENTS
Faisal Yaseen, MSEE, MHI, has been appointed to the JAMIA Student Editorial Board for 2024-2025.
January 22 – January 26, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Thomas Payne, MD, FACP, FACMI, FAMIA, FIAHSI
Thursday, February 1st – 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: Applying machine learning models in clinical care: Lessons from the Epic Sepsis Model
Abstract: Early detection and treatment can reduce mortality from sepsis, which is a leading cause of hospital death. There is enormous excitement about using machine learning models to identify patients at high risk for sepsis so they can be promptly treated. Most hospitals use electronic alerting systems and hundreds of Epic customers use its Epic Sepsis Model. Have we succeeded in reducing harm by leveraging EHR data and this broadly used alerting system? What lessons can be learned from this example that can help us apply machine learning models to address the many problems our health care systems face?
Presenter Bio: Thomas Payne is a primary care internist, Professor of Medicine and of Biomedical Informatics, and for 20 years served as Medical Director for Information Technology Services at University of Washington Medicine. He is attending physician at UW Medical Center and Harborview Medical Center in both inpatient and outpatient care. He is past Board Chair of the American Medical Informatics Association (AMIA). He chaired the AMIA EHR 2020 Task Force and testified before the US Senate HELP Committee on its contents. He is on the faculty of the AMIA Clinical Informatics Board Review course, edits a textbook on clinical computing systems and is author of over 100 articles in informatics. He is Senior Editor of Applied Clinical Informatics and on the editorial board of JAMIA and JAMIA Open.
PAPERS & PRESENTATIONS
C. Nau, R.K. Butler, C. Huang, V.K. Khang, A. Chen, B. Creekmur, B. Broder, C. Subject, A.L. Sharp, L.M. Moreta-Sainz, J.S. Park, A.J. Manek, R.M. Cooper, S.M. Mendoza, G. Luo, M.K. Gould. Development and Validation of the COVID-19 Hospitalized Patient Deterioration Index. American Journal of Managed Care, Vol. 29, No. 12, 2023, pp. e365-e371.
ANNOUNCEMENTS
A team, including BIME faculty member Patrick Wedgeworth, received pilot funding through the SCRC’s Collaborating Scholars and Pilot Grant Program for interdisciplinary suicide care research.
January 15 – January 19, 2024
UPCOMING LECTURES AND SEMINARS
BIME 590
More information coming soon!
Thursday, January 25th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Zoom Information: https://washington.zoom.us/my/bime590
ANNOUNCEMENTS
Andrea Hartzler was interviewed by STAT news about her team’s 2023 JAMIA article on incorporating patient voices to inform the automated extraction of SDoH from clinical notes: “Patients’ social needs often get lost in health records. Generative AI could help” (Jan 11 2024). BIME team members include Patrick Wedgeworth, Serena Xie, Carolin Spice, Raina Langevin, BIME alum Kevin Lybarger, and BIME affiliate Angad Singh.
Sean Mooney Webinar!
Please join us on Thursday, February 1 at noon, as we hear from Dr. Sean Mooney from University of Washington. Dr. Mooney will be discussing, “Facilitating and Studying the Use of Artificial Intelligence in Healthcare.”
To register, click here.
January 8 – January 12, 2024
PAPERS & PRESENTATIONS
Simms, A.M., Kanakia, A., Sipra, M. et al. A patient safety knowledge graph supporting vaccine product development. BMC Med Inform Decis Mak 24, 10 (2024). https://doi.org/10.1186/s12911-023-02409-8
Xie SJ, Mah NR, Chew L, Ruud J, Hernandez J, Lowery J, Hartzler AL. Improving vaccine equity: How community engagement and informatics facilitate health system outreach to underrepresented groups. Applied Clinical Informatics. Paper in press.
ANNOUNCEMENTS
The Fort Peck Tribes invited a small team of Googlers to visit Fort Peck Reservation in Poplar, Montana, for two days of bidirectional learning and relationship building to support developing inclusive and socially beneficial technology, including Artificial Intelligence (AI). The team was led on the Fort Peck side by Kenny Smoker and on the Google side by BIME affiliate faculty, Diane Korngiebel.
January 1 – January 5, 2024
PAPERS & PRESENTATIONS
Phuong J, Ordóñez P, Cao J, Moukheiber M, Moukheiber L, Caspi A, Swenor BK, Naawu DK, Mankoff J. Telehealth and digital health innovations: A mixed landscape of access. PLOS Digital Health. 2023 Dec 15;2(12):e0000401. https://doi.org/10.1371/journal.pdig.0000401
Espinoza JC, Sehgal S, Phuong J, Bahroos N, Starren J, Wilcox A, Meeker D. Development of a Social and Environmental Determinants of Health Informatics Maturity Model. Journal of Clinical and Translational Science.:1-27. https://doi.org/10.1017/cts.2023.691
Guo Y, Qiu W, Leroy G, Wang S, Cohen T. Retrieval augmentation of large language models for lay language generation. Journal of Biomedical Informatics. 2023 Dec 30:104580. https://www.sciencedirect.com/science/article/pii/S1532046423003015
ANNOUNCEMENTS
Looking ahead…
Thomas Payne, MD, FACP, will retire at the end of Spring Quarter 2024. He will still continue with some research activities, but will not be continuing clinical care or teaching courses.
December 4-8, 2023
PAPERS & PRESENTATIONS
Thomas H Payne, Grace K Turner. I’m not burned out. This is how I write notes, JAMIA Open, Volume 6, Issue 4, December 2023, ooad099, https://doi.org/10.1093/jamiaopen/ooad099
November 27 – December 1, 2023
UPCOMING LECTURES AND SEMINARS
BIME 590
Presenter: Yifan Peng
Thursday, December 7th – 11-11:50 am
850 Republican Street, Building C, Room 123 A/B
Presenter will present via Zoom
Zoom Information: https://washington.zoom.us/my/bime590
Title: LLMs and Evidence Summarization
Abstract: Generative AI, exemplified by large language models (LLMs) shows great promise in assisting medical evidence summarization. However, concerns have been raised about the quality of outputs generated by pre-trained LLMs, which potentially results in harmful misinformation. In this talk, I will first discuss our investigation into the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. Our study demonstrates that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Furthermore, we observe that automatic metrics often do not strongly correlate with the quality of summaries. I will then discuss our research on the impact of fine-tuning LLMs to enhance their performance in evidence summarization. We found that compared to zero-shot learning, the fine-tuned LLMs improved the automatic evaluation metrics such as ROUGE, METEOR, CHRF, and PICO-F1. We also found that smaller fine-tuned models sometimes demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were also manifested in both human and GPT4-simulated evaluations. Our findings confirmed the potential for LLMs to empower medical evidence summarization.
Presenter Bio: Yifan Peng, PhD, is an Assistant Professor in the Division of Health Sciences Department of Population Health Sciences at Weill Cornell Medicine. He graduated from UD in 2016, under the supervision of Dr. Cathy Wu and Dr. Vijay Shanker. His main research interests include BioNLP and medical image analysis. He has published in major AI and healthcare informatics venues, including ACL, CVPR, MICCAI, and ICHI, as well as medical venues, including Nature Medicine, Nucleic Acids Research, npj Digital Medicine, and JAMIA. His research has been funded by federal agencies, including NIH and NSF and industries such as Amazon and Google. He is an Editorial Board Member for the Journal of Biomedical Informatics. He received the AMIA New Investigator Award in 2023.
PAPERS & PRESENTATIONS
X. Zhang, S.B. Zeliadt, G. Luo, J. Toyama, S.S. Coggeshall, and S. Taylor. Experience of Veterans with Chronic Pain Initiating CIH therapies across 18 VHA Medical Centers Following the Initial COVID-19 Pandemic. 2024 International Congress on Integrative Medicine and Health, Cleveland, OH, Apr., 2024.
Cody M. Schopf, Ojas A. Ramwala, Kathryn P. Lowry, Solveig Hofvind, M. Luke Marinovich, Nehmat Houssami, Joann G. Elmore, Brian N. Dontchos, Janie M. Lee, Christoph I. Lee, Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review, Journal of the American College of Radiology, 2023, ISSN 1546-1440, https://doi.org/10.1016/j.jacr.2023.10.018.
Nicholas J Dobbins, Bin Han, Weipeng Zhou, Kristine F Lan, H Nina Kim, Robert Harrington, Özlem Uzuner, Meliha Yetisgen, LeafAI: query generator for clinical cohort discovery rivaling a human programmer, Journal of the American Medical Informatics Association, Volume 30, Issue 12, December 2023, Pages 1954–1964, https://doi.org/10.1093/jamia/ocad149
GENERAL EXAMS
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.
ANNOUNCEMENTS
Oliver Bear Don’t Walk was elected to AMIA’s Board of Directors.
Casey Overby Taylor was promoted to associate professor of Medicine and Biomedical Engineering at Johns Hopkins University School of Medicine.
Dr. Bryant thomas Karras, MD, was recently inducted as a fellow into the American College of Medical Informatics (ACMI). ACMI is an honorary College of elected Informatics Fellows from the United States and abroad who have made significant and sustained contributions to the field of medical informatics and who have met rigorous scholarly scrutiny by their peers. The FACMI designation is one of distinction and pride. They represent excellence from academia, government and industry and are the best and brightest stars in our field demonstrating thought leadership, stellar experience, and established scholarship. Together their important contributions to biomedicine and healthcare inform, educate, and inspire the informatics community to improve human health.
Dr. Karras, a Physician, a Bio-Medical Engineer, and Sr.-Medical Epidemiologist, joined Washington State’s Department of Health in 2007 following an academic appointment at UW. He led the cross-divisional efforts to prepare public health for meaningful use and dramatic changes to PH practice that Statewide Health Information Exchange and HealthIT regulation brought. As Chief Medical Informatics Officer (CMIO), he guides the agencies interoperability and informatics enterprise-wide effort and data modernization strategy. Dr. Karras was a founding faculty member of UW’s Biomedical & Health Informatics program, and the Center for Public Health Informatics where he led development of the original Competencies for PH Informaticians. He is active in public health practice nationally (CDC ONC CSTE AIRA ASTHO) and internationally (HIMSS HL7 WHO PAHO G20 GDHP) Taskforce for Global Health’s PH Informatics Institute (PHII) and serving on national Federal Advisory Committees to HHS (https://www.healthit.gov/hitac/member/bryant-thomas-karras ), always a strong voice for PH Informatics. During the pandemic, he successful developed and implemented new innovations such as Bluetooth Exposure Notification (WA Notify) and SMART Health Cards (WAverify.org). Evaluation and development of these novel and continuing innovations are in close collaboration with the UW (SPH, SON, Global Health, and CS), MITRE, Microsoft, AWS, Apple, and Google.
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.
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/.