Software for Distributed Computation on Medical Databases: A Demonstration Project

TL;DR

This paper presents software tools built on R that facilitate distributed computation on medical databases, enabling collaborative modeling while respecting privacy and data heterogeneity.

Contribution

The paper introduces a flexible, open-source software framework for distributed statistical modeling across heterogeneous medical databases, supporting privacy and collaboration.

Findings

Successful implementation of site-stratified Cox model.

Effective distributed computation of rank-k SVD.

Software supports heterogeneous database environments.

Abstract

Bringing together the information latent in distributed medical databases promises to personalize medical care by enabling reliable, stable modeling of outcomes with rich feature sets (including patient characteristics and treatments received). However, there are barriers to aggregation of medical data, due to lack of standardization of ontologies, privacy concerns, proprietary attitudes toward data, and a reluctance to give up control over end use. Aggregation of data is not always necessary for model fitting. In models based on maximizing a likelihood, the computations can be distributed, with aggregation limited to the intermediate results of calculations on local data, rather than raw data. Distributed fitting is also possible for singular value decomposition. There has been work on the technical aspects of shared computation for particular applications, but little has been published on the software needed to support the "social networking" aspect of shared computing, to reduce the barriers to collaboration. We describe a set of software tools that allow the rapid assembly of a collaborative computational project, based on the flexible and extensible R statistical software and other open source packages, that can work across a heterogeneous collection of database environments, with full transparency to allow local officials concerned with privacy protections to validate the safety of the method. We describe the principles, architecture, and successful test results for the site-stratified Cox model and rank-k Singular Value Decomposition (SVD).