Identification of neuropsychiatric signs and symptoms and exploration of the labeled data
We have established a computational pipeline that consists of text parsers and NLP models to convert the extensive medical record summaries into clinical disease trajectories (Fig. 1a). This pipeline consists of three steps, with the first parsing NBB donor files, the second defining and predicting attributes in the clinical history (Extended Data Table 1) and converting the predicted signs and symptoms into clinical disease trajectories, and the third using the trajectories for downstream analyses. In total, we included 3,042 donor files from donors with various NDs (Extended Data Fig. 1a, Table 1 and Supplementary Tables 1 and 2).
a, Workflow of the project describing the different data types in the NBB donor files (i), the processing of the clinical history…
