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"Beyond mountains, there are mountains." — Haitian proverb
The Freda Lab is based in the Department of Computational Biomedicine at Cedars-Sinai Health Sciences University in Los Angeles, CA.
<p>We develop AI-driven methods that make clinical data more usable for prediction, discovery, and decision support. Our work sits at the intersection of clinical informatics, machine learning, and translational research, with a focus on building automated (and increasingly agentic) pipelines that transform noisy EHR data into clinically actionable representations.</p>
<p>Major themes of our research include improving risk identification for adverse spine surgery outcomes and for problematic opioid use/opioid use disorder, leveraging structured EHR elements, clinical narratives, imaging, and social determinants of health to model patient risk and heterogeneity. We also design knowledge-graph–based frameworks that capture relationships among clinical concepts, data quality operations, and model behavior to support transparent, auditable AI workflows and human–AI collaboration.</p>
<p>In parallel, we develop automated machine learning frameworks that leverage evolutionary algorithms for genotype-to-phenotype association analysis, with specific emphasis on detecting epistatic and other non-additive genetic effects that traditional approaches often miss.</p>
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- Agentic AI and automation for EHR data processing (data cleaning, feature engineering, reproducible pipelines)
- Phenotyping and risk modeling from structured + unstructured EHR data (including NLP)
- Clinical prediction for perioperative risk and outcomes in elective spine surgery
- Computational phenotyping of problematic opioid use / OUD from clinical notes and discharge summaries
- Severity characterization and context-aware NLP/annotation frameworks to support downstream modeling
- Knowledge graph development to represent clinical concepts, data transformations, and model evidence
- Retrieval-augmented and graph-informed interfaces to improve interpretability, traceability, and actionability
- Quantifying patient subgroup structure and outcome heterogeneity (e.g., clustering approaches) in surgical populations
- Integrating and operationalizing social determinants data in EHR systems
Interested in our research or collaboration? Explore our research areas, recent publications, or meet the team. Feel free to reach out!
