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Hi! I’m Hannah, a software engineer with 8 years of experience & Biomedical Informatics Ph.D. with 14 peer-reviewed publications.

I recently graduated with my Ph.D. in biomedical and health informatics from the University of Washington. I am now looking for opportunities leveraging my broad technical and research skills and subject matter expertise toward innovative and impactful technical problems in the digital health space.

I am a software engineer and biomedical informatics researcher with 8 years of experience. I have 4 years of professional software engineering experience working for the leading electronic health records company. In this role, I worked with a high level of autonomy, leading several projects and initiatives, teaching programming to non-technical staff, and mentoring interns and new hires. I have an additional 4 years of experience conducting applied informatics research, including software development and data science for a range of projects. My research focused, among other things, on machine learning and deep learning, natural language processing for mental health applications, FHIR-based application development for COVID-19 symptom monitoring as well as for suicide prevention, and user research (needs assessments). As a graduate research assistant, I also consulted as an informatician and biostatistician for several clinical research projects. My research has resulted in 14 peer-reviewed publications so far, with more underway. Finally, I am currently a biomedical informatics consultant for a large health system in Washington, developing guidance for a biomedical informatics research initiative aiming to capitalize on extensive electronic health records data.

As an NLM Predoctoral Fellow in the Biomedical and Health Informatics (BHI) program at the University of Washington, I worked with Trevor Cohen, Bill Lober and the Clinical Informatics Research Group (CIRG), Andrea Hartzler, Kate Comtois, Kari Stephens, and Matthew Thompson. I am interested in clinical informatics, especially clinical decision support and predictive analytics; mobile health; distributional representations and semantics; mental health informatics; and data science and machine learning for all things BHI.

Check out my LinkedIn page!

Curriculum Vitae

Dissertation: Burkhardt, H. A. (2022). Needs-driven, utility-oriented, standards-based operationalization of artificial intelligence for clinical decision support: a framework with application to suicide prevention. [PDF]

Journal & Conference Publications

Burkhardt, H. A., Laine, M., Kerbrat, A., Cohen, T., Comtois, K. A., & Hartzler, A. (2022). Identifying opportunities for informatics-supported suicide prevention: the case of Caring Contacts. Proceedings of the AMIA Annual Symposium 2022. Distinguished Paper Award.

Burkhardt, H. A., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. (2022). Comparing emotion feature extraction approaches for predicting depression and anxiety. NAACL 2022. Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology. [Paper] [Poster] [Slides]

Burkhardt, H. A., Alexopoulos, G. S., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. (2021). Behavioral Activation and Depression Symptomatology: Longitudinal Assessment of Linguistic Indicators in Text-Based Therapy Sessions. Journal of Medical Internet Research, 23(7), e28244. https://doi.org/10.2196/28244

Burkhardt, H., Brandt, P., Lee, J., Karras, S., Bugni, P., Cvitkovic, I., Chen, A., & Lober, W. (2021). StayHome: A FHIR-Native Mobile COVID-19 Symptom Tracker and Public Health Reporting Tool. Online Journal of Public Health Informatics, 13(1). https://doi.org/10.5210/ojphi.v13i1.11462 (Open Access) [PDF]

Thomas, J. A.* and Burkhardt, H. A.*, Chaudhry, S., Ngo, A. D., Sharma, S., Zhang, L., Au, R., Hosseini Ghomi, R. (2020). Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data. Journal of Alzheimer’s Disease: JAD, 76(3), 905–922. https://doi.org/10.3233/JAD-190783. (* indicates equal contributions)

Burkhardt, H. A., Subramanian, D., Mower, J., & Cohen, T. (2019). Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications. Proc AMIA Annu Symp 2019. Knowledge Discovery and Data Mining Student Innovation Award. [Manuscript on AMIA conference website] [Read on BioRxiv]

Poster Presentations

Burkhardt, H. A., Prado, M. G., Kessler, L. G., Au, M. A., Zigman Suchsland, M., Kowalski, L., Stephens, K. A., Yetisgen, M., Walter, F. M., Neal, R. D., Lybarger, K., Thompson, C. A., Achkar, M. Al, Sarma, E. A., Turner, G., Farjah, F., & Thompson, M. (2022). Signs and symptoms extracted from Electronic Health Records with AI predict lung cancer diagnosis at least 6 months in advance. The Early Detection of Cancer Conference.[[PDF]][2022-10-04%20Early%20detection%20conference%20poster%20final.pdf]

Burkhardt, H.A., Portanova, J. (2021) Teaching issues of research reproducibility through first-hand experience. 17th Annual UW Teaching and Learning Symposium.

Burkhardt, H.A., Dobbins, N., Mollis, B., Au, M., Kwan Ma, K.P., Yetisgen, M., Singh, A., Thompson, M., Stephens, K.A. (2021). Extracting COVID-19 Related Symptoms from EHR Data: A Comparison of Three Methods. AMIA Virtual Informatics Summit 2021. [PDF]

Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic Predications.
2019 NLM Training Conference (Indianapolis, IN). June 25, 2019.[PDF]

Talks

Linguistic markers of behavioral activation predict changes in depression symptomatology. Burkhardt, H. A., Alexopoulos, G. S., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. 2021 NLM Training Conference (Virtual, hosted by University of Washington). June 23, 2021.

The Unreasonable Effectiveness of Naïve Bayes for Predicting Combinatorial Drug Effects. Burkhardt, H. A., Subramanian, D., Mower, J., & Cohen, T. 2020 NLM Training Conference (Virtual, hosted by Oregon Health & Science University). June 24, 2020.

StayHome: A FHIR-Native Mobile COVID-19 Symptom Tracker and Public Health Reporting Tool. University of Washington FHIR Conference, Seattle, WA. September 2020.

Mobile PROs with FHIR. Seattle on FHIR Meetup. October 2019.
[Notes and Recording]