Faculty, Staff and Student Publications
Language
English
Publication Date
12-1-2023
Journal
Proceedings of Machine Learning Research
PMID
39624658
PMCID
PMC11611252
Abstract
Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications including Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., different patients in EHRs may have different length of records. PARAFAC2 has been successfully applied to EHRs for extracting meaningful medical concepts (phenotypes). Despite recent advancements, current models' predictability and interpretability are not satisfactory, which limits its utility for downstream analysis. In this paper, we propose MULTIPAR: a supervised irregular tensor factorization with multi-task learning for computational phenotyping. MULTIPAR is flexible to incorporate both static (e.g. in-hospital mortality prediction) and continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks. We conduct extensive experiments on two real-world temporal EHR datasets to demonstrate that MULTIPAR is scalable and achieves better tensor fit with more meaningful subgroups and stronger predictive performance compared to existing state-of-the-art methods. The implementation of MULTIPAR is available.
Keywords
tensor factorization, electronic health records, PARAFAC2, multi-task learning
Published Open-Access
yes
Recommended Citation
Ren, Yifei; Lou, Jian; Xiong, Li; et al., "MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning for Computational Phenotyping" (2023). Faculty, Staff and Student Publications. 733.
https://digitalcommons.library.tmc.edu/uthshis_docs/733