TASTE

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).

Yanxin Ye processed the CMS data using Hive.

Xinze Wang did case-control mapping using Pymatch.

Xingchi Li translated MATLAB code of TASTE and Nonnegative matrix factorization (NMF) algorithms based on alternating non-negativity constrained least squares to Python then did compatibility modification.

Hung-Yi Li did the cross validation and numerous experiments to do better comparison.

Previous TASTE code can be found in legacy which is implemented in MATLAB by Ardavan (Ari) Afshar.

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

Relevant Publication

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.

Code description

Nonnegative matrix factorization (NMF) algorithms based on alternating non-negativity constrained least squares has been imported and translated in nonnegfac.py.

There are two main functions defined in main.py: fit(R, A, X) and project(R, A, X, V, F, H):

R denotes the number of phenotypes
A denotes the static feature matrix
X denotes the temporal feature matrix
V, F, H are the matrices obtained from the fit function.

In Memory of Ardavan (Ari) Afshar

Ari is a good mentor in directing us with this project. It is really sad to see a good person not around any more. RIP Ari, you will be missed. Kudoboard

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