TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. It performs two import tasks in healthcare: 1- computational phenotyping 2- Predictive modeling by analyzing electronic health records (EHRs).

TASTE applied on dynamically-evolving structured EHR data and static patient information. Each represents the medical features recorded for different clinical visits for patient . Matrix includes the static information (e.g., race, gender) of patients. TASTE decomposes into three parts: , , and . Static matrix is decomposed into two parts: and . Note that (personalized phenotype scores) is shared between static and dynamically-evolving features.

TASTE implements the code in the following paper:

Afshar, Ardavan, Ioakeim Perros, Haesun Park, Christopher deFilippi, Xiaowei Yan, Walter Stewart,
Joyce Ho, and Jimeng Sun. "TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic
Health Records." ACM CHIL 2020.

Before running the codes you need to import the following packages:

• Tensor Toolbox Version 2.6 which can be downloaded from: https://www.sandia.gov/~tgkolda/TensorToolbox/index-2.6.html
• Nonnegative matrix factorization (NMF) algorithms based on alternating non-negativity constrained least squares (https://www.cc.gatech.edu/~hpark/nmfsoftware.php).

To start with you need to run: "main.m" file.

If you find any bug in the codes or face any issue please feel free to contact me at [email protected]