A toolbox for predicting clinical outcome of Alzheimer’s Diseases. It will generally run with Edward on top of tensorflow.

Main tools

Train_model: Load the training dataset, train the prediction model and save the model.

Test_model: Load the testing dataset, run the trained model on different testing subjects and save the results.

Idx: The subject ID we used from https://tadpole.grand-challenge.org/data/

Read_data: Read the raw dataset and convert to our input files.

test-run.ipynb: The juypter notebook file to run the train_model.py and test_model.py.

Y1: MMSE (0-30)

Y2: ADAS-COG(0-70)

Y3: normalized ventricle volume

Y4: normalized hippocampus volume

Y5: CDRSB(0-17).

X: APOE4, gender, education, normalized ventricle volume and hippocampus volume at baseline visit

Requirements: • Tensorflow, Edward and all of their requirements (e.g. hyp5). Please install following packages to run our code: tensorflow: https://www.tensorflow.org Edward: http://edwardlib.org/ • numpy, scipy, tqdm Development: Please contact yingying zhu, yz2377@cornell.edu for question related.

Papers: If you use this code, please cite one of the following: Yingying Zhu, Mert R. Sabuncu, A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome, Medical Image Computing and Computer-Assisted Intervention (MICCAI) workshop, 2018.