A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data
This work addresses Alzheimer's disease prediction for patients, but it is incremental as it combines existing methods for data fusion and survival analysis.
The authors tackled predicting progression from mild cognitive impairment to Alzheimer's disease by fusing anatomical shape and clinical data in a neural network, achieving improved performance over models using shape or biomarkers alone.
We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop Alzheimer's disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient's hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer's Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.