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 25,000,000 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. Bridging semantic gaps to integrate real-world clinical data with scientific knowledge graphs
3. Domain-agnostic machine learning approaches for detecting biases and discovering biomedical insights in electronic health record (EHR) data

Research Focus

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.