Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction

By Yuan Luo, F. Ahmad, Sanjiv J. Shah. 2017

46 citations recorded by the source corpus.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome that may benefit from improved subtyping in order to better characterize its pathophysiology and to develop novel targeted therapies. The United States Precision Medicine Initiative comes amid the rapid growth in quantity and modality of clinical data for HFpEF patients ranging from deep phenotypic to trans-omic data. Tensor factorization, a form of machine learning, allows for the integration of multiple data modalities to derive clinically relevant HFpEF subtypes that may have significant differences in underlying pathophysiology and differential response to therapies. Tensor factorization also allows for better interpretability by supporting dimensionality reduction and identifying latent groups of data for meaningful summarization of both features and disease outcomes. In this narrative review, we analyze the modest literature on the application of tensor factorization to related biomedical fields including genotyping and phenotyping. Based on the cited work including work of our own, we suggest multiple tensor factorization formulations capable of integrating the deep phenotypic and trans-omic modalities of data for HFpEF, or accounting for interactions between genetic variants at different omic hierarchies. We encourage extensive experimental studies to tackle challenges in applying tensor factorization for precision medicine in HFpEF, including effectively incorporating existing medical knowledge, properly accounting for uncertainty, and efficiently enforcing sparsity for better interpretability.

Subfields: Cardiology and Cardiovascular Medicine, Computational Mathematics, Genetics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Yuan Luo

In Computational Mathematics

1. Zexian Zeng
2. Yuan Luo
3. Xiaoyu Li
4. Yu Deng
5. Tristan Naumann

In Genetics

1. Feixiong Cheng
2. Joel T. Dudley
3. Yuan Luo
4. Xiaoyu Li
5. Yu Deng
6. Zexian Zeng
7. Euan A. Ashley
8. Tristan Naumann

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[69] has explored EHR phenotyping algorithms to facilitate GWAS.”
• Multimodal machine learning in precision health: A scoping review (2022, background): “The clinical area of heart failure with preserved ejection fraction (HFpEF) saw novel applications of multiple tensor factorization formulations to integrate the deep phenotypic and trans-omic information 24 , and this extends to other areas of precision medicine 25 .”
• Artificial Intelligence in Cardiology. (2018, background): “Shah SJ, Katz DH, Deo RC. Phenotypic spectrum of heart failure with preserved ejection fraction.”