# Federated Tensor Factorization for Computational Phenotyping

## TL;DR

This paper introduces a federated tensor factorization method that enables multiple hospitals to collaboratively derive clinical phenotypes from electronic health records without sharing sensitive patient data, maintaining privacy while achieving accurate results.

## Contribution

The paper presents a novel federated tensor factorization approach using ADMM, allowing hospitals to jointly analyze data without sharing patient-level information.

## Key findings

- Achieves similar accuracy to centralized models
- Respects patient privacy through secure data sharing
- Effective in real medical datasets

## Abstract

Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.