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Hadlock Lab Overview

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“Accelerating translational research by integrating clinical data into systems biology at scale”

–Assistant Professor, Jennifer Hadlock, MD
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Research Overview

The Hadlock lab collaborates with systems biologists, data scientists, clinicians and population health experts to understand transitions between wellness and disease. We conduct translational research to improve risk models for clinical decision support and investigate novel methods to accelerate research discovery. Our lab works directly with two types of clinical data: 1) over 19,000,000 electronic health records (EHRs) from 52 hospitals and 1085 clinics across five states, and 2) high-fidelity prospective study data that combines genomics, imaging, health-related social needs and patient-reported outcomes. We collaborate closely with experts at research institutions and community hospitals across the country. Our current investigations focus on three areas:

  1. Supervised machine learning for biomedically interpretable models for clinical decision support
  2. Domain-agnostic machine learning approaches for detecting biases and discovering biomedical insights in electronic health record (EHR) data
  3. Bridging semantic gaps to integrate real-world clinical data with scientific knowledge bases

Research Focus

Machine learning for biomedical, interpretable risk models for clinical decision support

Risk scores aim to support medical personnel when they face complex, multifactorial and high-stakes clinical decisions about optimal patient care. The Hadlock Lab is developing more accurate, clinically relevant risk models to inform individual patient care for prevention, screening, treatment and follow-up. We integrate machine learning, biomedical knowledge ontologies, and clinical expertise to analyze EHR data. These approaches are designed to be extensible across many patient conditions, and we are currently investigating early risk stratification for immune-mediated inflammatory disease, pregnancy outcomes, including analyses with COVID-19. We collaborate with the medical research experts at Providence St. Joseph Health and the University of Washington to prioritize risk models that have the potential to directly improve patient care. Our current investigations focus on stratification at three different time points: primary and secondary prevention, early symptom recognition, and pre-hospital transport.

Bridging the semantic gap between real-world clinical data and knowledge graphs

Bioinformatics, clinical informatics, and scientific knowledge bases continue to advance rapidly. However, many challenges remain to integrate this disparate data for research. For our work on specific conditions, we address challenges with semantic interoperability by applying existing, well-curated ontologies, and when additional accuracy is needed, expert-reviewed maps. The Hadlock Lab is part of the NIH NCATS Biomedical Data Translator, collaborating with researchers to connect clinical data with over twenty other categories of data, including multi-omics, drugs and environmental exposures.

Accelerating biomedical discovery in electronic health records (EHRs) and longitudinal studies of health transitions

Understanding similarity between patients is a fundamental concept underlying clinical care and biomedical research. Our lab uses distance metrics to analyze similarity over 300,000,000 patient encounters. We are currently collaborating with several labs at ISB to apply new domain-agnostic approaches for rapid discovery of patterns in semi-structured clinical data. The patterns that emerge can include several categories of new insights: hypotheses for biomedical research, artifacts of healthcare delivery, error and biases. By focusing directly on clinical observations of phenotype and exposures in the real world, we can minimize the noise introduced by mapping investigations through single diagnostic labels, and increase the chance of detecting previously unobserved patterns. Once surfaced, these hypotheses can be prioritized for rigorous investigation using existing research methods.

Publications

Diaz, George A, Alyssa B Christensen, Tobias Pusch, Delaney Goulet, Shu-Ching Chang, Gary L Grunkemeier, Paul A McKelvey, et al. 2021. “Remdesivir and Mortality in Patients With Coronavirus Disease 2019.” Clinical Infectious Diseases, ciab698. https://doi.org/10.1093/cid/ciab698. Cite Download
Jiang, Yonghou, Fergal Duffy, Jennifer Hadlock, Andrew Raappana, Sheila Styrchak, Ingrid Beck, Fred D. Mast, et al. 2021. “Angiotensin II Receptor I Auto-Antibodies Following SARS-CoV-2 Infection.” PloS One 16 (11): e0259902. https://doi.org/10.1371/journal.pone.0259902. Cite Download
Lee, Jihoon W., Yapeng Su, Priyanka Baloni, Daniel Chen, Ana Jimena Pavlovitch-Bedzyk, Dan Yuan, Venkata R. Duvvuri, et al. 2021. “Integrated Analysis of Plasma and Single Immune Cells Uncovers Metabolic Changes in Individuals with COVID-19.” Nature Biotechnology. https://doi.org/10.1038/s41587-021-01020-4. Cite Download
Lee, Jewel Y., Sevda Molani, Chen Fang, Kathleen Jade, D. Shane O’Mahony, Sergey A. Kornilov, Lindsay T. Mico, and Jennifer J. Hadlock. 2021. “Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study.” JMIR Medical Informatics 9 (7): e29986. https://doi.org/10.2196/29986. Cite Download
Su, Yapeng, Dan Yuan, Daniel G. Chen, Kai Wang, Jongchan Choi, Chengzhen L. Dai, Sunga Hong, et al. 2021. “Heterogeneous Immunological Recovery Trajectories Revealed in Post-Acute COVID-19.” MedRxiv, 2021.03.19.21254004. https://doi.org/10.1101/2021.03.19.21254004. Cite Download
Dai, Chengzhen L., Sergey A. Kornilov, Ryan T. Roper, Hannah Cohen-Cline, Kathleen Jade, Brett Smith, James R. Heath, et al. 2021. “Characteristics and Factors Associated with COVID-19 Infection, Hospitalization, and Mortality Across Race and Ethnicity.” Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. https://doi.org/10.1093/cid/ciab154. Cite Download
Goldman, Jason D., Kai Wang, Katharina Röltgen, Sandra C. A. Nielsen, Jared C. Roach, Samia N. Naccache, Fan Yang, et al. 2020. “Reinfection with SARS-CoV-2 and Failure of Humoral Immunity: A Case Report.” https://www.medrxiv.org/content/10.1101/2020.09.22.20192443v1. Cite Download
Su, Yapeng, Daniel Chen, Dan Yuan, Christopher Lausted, Jongchan Choi, Chengzhen L. Dai, Valentin Voillet, et al. 2020. “Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19.” Cell, S0092867420314446. https://doi.org/10.1016/j.cell.2020.10.037. Cite Download
Fecho, Karamarie, Stanley C. Ahalt, Saravanan Arunachalam, James Champion, Christopher G. Chute, Sarah Davis, Kenneth Gersing, et al. 2019. “Sex, Obesity, Diabetes, and Exposure to Particulate Matter among Patients with Severe Asthma: Scientific Insights from a Comparative Analysis of Open Clinical Data Sources during a Five-Day Hackathon.” Journal of Biomedical Informatics 100: 103325. https://doi.org/10.1016/j.jbi.2019.103325. Cite
Ahalt, Stanley C., Christopher G. Chute, Karamarie Fecho, Gustavo Glusman, Jennifer Hadlock, Casey Overby Taylor, Emily R. Pfaff, et al. 2019. “Clinical Data: Sources and Types, Regulatory Constraints, Applications.” Clinical and Translational Science. https://doi.org/10.1111/cts.12638. Cite Download
Robinson, Max, Jennifer Hadlock, Jiyang Yu, Alireza Khatamian, Aleksandr Y. Aravkin, Eric W. Deutsch, Nathan D. Price, Sui Huang, and Gustavo Glusman. 2018. “Fast and Simple Comparison of Semi-Structured Data, with Emphasis on Electronic Health Records.” BioRxiv, 293183. https://doi.org/10.1101/293183. Cite Download