Research

1. Natural Language Processing (NLP): Vast amounts of unstructured data are generated in our electronic health records (EHRs), including clinical notes, pathology reports, radiology reports and operative notes. Manual and retrospective review of key clinical endpoints from these texts is labor-intensive, time-consuming and error-prone. We are developing and evaluating natural language processing models to automate the extraction of critical oncologic clinical variables from unstructured clinical documents and to improve the quality of our research data.
2. Synthetic Data Generation: Access to large, comprehensive datasets is critical for research and the development of predictive models. Access to relevant clinical datasets is often limited by patient privacy concerns, data silos and the lack of high-quality data, particularly for rare cancer research. We are utilizing existing data to develop and implement advanced techniques for synthetic data generation. We hope to utilize this methodology to overcome data scarcity, facilitate data sharing, test hypotheses and simulate clinical trials.
3. Automated Cancer Data Initiative (ACDI): Unstructured data entry into EHRs create significant challenges when collecting phenotypic or clinical endpoints for clinical research. Retrospective collection of data is limited by lack of standardization and significant bias. Manual data collection is also time consuming and prone to errors. We are implementing a comprehensive structured data entry system that allows us to prospectively collect clinical data and ensure high-quality data for research and clinical decision making.

If you are interested in collaborating on any research projects, please contact us.