Synthetic Data Enhanced Prediction of the neuropathological landscape of dementia before death using multimodal AI
Abstract
Pathology-Aware Representation Learning, Prediction, and Synthetic Data in NACC-Style CohortsThis repository contains the full analysis pipeline and supporting code for pathology-aware representation learning, neuropathology prediction, and DDPM-based synthetic patient generation in NACC-style datasets, together with harmonized external cohorts (ROSMAP and Munich Neurobiobank).The deposit is organized into three main components:Main Clean Code.ipynb – final integrated TabPFN/SEM/DDPM pathology pipeline and prediction workflows.1Rosmap&NbmDtata2NaccStyle.ipynb – harmonization of ROSMAP and Munich Neurobiobank into NACC-compatible tables and joint latent-factor export.1RawCode.zip – archive of original scripts used during development, figure generation, and SHAP-based interpretation.1Additional model result bundles (e.g. DDPMModel&Result.zip, CoPathologyModel&Result.zip) provide trained models and evaluation outputs corresponding to the pipelines in the main notebook.11. Main Clean Code.ipynbPurposeMain Clean Code.ipynb is the primary research pipeline. It composes several interlocking workflows for:TabPFN-based embedding learning from clinical features with explicit NACCID traceability.Construction of five normalized pathology latent variables (0–1) via TabPFN imputation, correlation filtering, and SEM.DDPM-based conditional synthetic patient generation over the 5D pathology latent space (healthy → maximal pathology).Downstream prediction pipelines for ordinal and continuous…
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