Freda Lab
Translational AI for Clinical Prediction & Informatics @ Cedars-Sinai
"Beyond mountains, there are mountains." — Haitian proverb
The Freda Lab is based in the Department of Computational Biomedicine at Cedars-Sinai Medical Center in Los Angeles, CA.
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.
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.
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.
Research Focuses
Clinical Informatics & AI for Real-World Data
- 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
Substance Use Disorders & Clinical NLP
- 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 Graphs & AI-Human Collaboration
- Knowledge graph development to represent clinical concepts, data transformations, and model evidence
- Retrieval-augmented and graph-informed interfaces to improve interpretability, traceability, and actionability
Patient Heterogeneity
- Quantifying patient subgroup structure and outcome heterogeneity (e.g., clustering approaches) in surgical populations
- Integrating and operationalizing social determinants data in EHR systems
Computational Genetics
- Genetic programming and evolutionary algorithm-based methods for genotype-to-phenotype association analysis
- Complex trait modeling with emphasis on epistasis, non-additivity, and gene-gene interactions
Interested in our research or collaboration? Explore our research areas, recent publications, or meet the team. Feel free to reach out!
