Unpacking Unstructured Data: Extracting Insights from Neuropathological Reports of Parkinson’s Disease Patients using Large Language Models


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Linking pathology data with molecular and clinical data allows for a deeper understanding of disease, more accurate diagnosis, and ultimately better patient treatment. Pathology data needs to be structured in order to achieve this.

The aim of this study was to make unstructured neuropathological data, located in the NeuroBioBank (NBB), follow FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, and investigate the potential of Large Language Models (LLMs) in wrangling unstructured neuropathological reports. By making the currently inconsistent and disparate data findable, our overarching goal was to enhance research output and speed.


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