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]