Longitudinal Siamese Network for clinical trajectory prediction
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Longitudinal Siamese Network for clinical trajectory prediction

  • Code modules

    • Trajectory prediction (can be used to predict other clinical tasks as well)

      • LSN/notebooks/LSN_sim_testcode.ipynb: Stand-alone notebook for testing LSN with simulated data
      • LSN/lib/lsn.py: LSN model class and useful functions for training and testing
      • LSN/notebooks/run_lsn.ipynb: notebook to train and test LSN model with real data
    • Trajectory modeling

      • LSN/notebooks/model_trajectories.ipynb: Clustering code for modeling longitudinal clinical trajectories and subsequent assignment to new subjects
  • LSN data flow

    • input (check notebooks for required data shapes)
      • MR: baseline + follow-up (e.g. 78x2 AAL CT values)
      • aux: apoe4 status + clinical scores (baseline + follow-up) + demographics (optional)
    • output (one-hot)
      • labels: trajectory / Dx / Px labels (binary and multiclass are supported)
  • Legacy dir has older code version along with useful notebook for mapping vertex-wise CIVET data into an atlas based ROIs.

  • Prereqs

    • python3.5+
    • tensorflow-gpu 1.4.1 (conda: conda install -c anaconda tensorflow-gpu)
    • sklearn
    • pandas
    • seaborn