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.
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.
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
fit(R, A, X) and
project(R, A, X, V, F, H):
Rdenotes the number of phenotypes
Adenotes the static feature matrix
Xdenotes the temporal feature matrix
Hare the matrices obtained from the
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