Longitudinal Siamese Network for clinical trajectory prediction
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Code modules
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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
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Trajectory modeling
- LSN/notebooks/model_trajectories.ipynb: Clustering code for modeling longitudinal clinical trajectories and subsequent assignment to new subjects
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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)
- input (check notebooks for required data shapes)
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Legacy dir has older code version along with useful notebook for mapping vertex-wise CIVET data into an atlas based ROIs.
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Prereqs
- python3.5+
- tensorflow-gpu 1.4.1 (conda: conda install -c anaconda tensorflow-gpu)
- sklearn
- pandas
- seaborn