Foundation Large Language Models

Our lab is a pioneer in harnessing advanced Language Models (LLMs), including but not limited to LLaMA and GPT, to extract valuable insights from diverse medical texts. From electronic health records to scientific literature, we employ these models to decode complex medical narratives and uncover hidden knowledge.

Unlike other LLMs, our models are specifically tailored to the biomedical domain with comprehensive domain-specific training data. This allows us to develop models that are more accurate and efficient in understanding and generating biomedical texts. Read more

DataMed

DataMed is a biomedical data search engine. Its goal is to discover data sets across data repositories or data aggregators. In the future it will allow searching outside these boundaries. DataMed supports the NIH-endorsed FAIR principles of Findability, Accessibility, Interoperability and Reusability of datasets with current functionality assisting in finding datasets and providing access information about them.

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Biomedical Knowledge Explorer

In the rapidly evolving field of biomedicine, researchers are inundated with an ever-growing corpus of publications, including research scientific articles, books, technical reports, and working papers. This massive biomedical knowledge poses a significant challenge in terms of efficient data navigation and exploration. While several search engines have been developed to improve the search and browsing of massive literature, gaps in efficient knowledge exploration and global understanding persist.

To address this gap, we propose BIKE (Biomedical Knowledge Explorer), a visual analytics tool leveraging a large language model approach to explore the semantic embedding space of massive publications. By combining advanced language processing techniques with intuitive visualizations, BIKE offers researchers a powerful means to navigate and comprehend the complex landscape of biomedical literature.

NLP for Real World Studies

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies. Read more