Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes

TL;DR

This paper investigates the use of backdoor adjustment to mitigate confounding by provenance in multi-institutional clinical note classification, improving model robustness across different data sources.

Contribution

It introduces a backdoor adjustment method tailored for clinical NLP to address confounding biases caused by source-specific data distributions.

Findings

Backdoor adjustment reduces confounding bias in clinical text classification.

The method improves robustness of models across different institutions.

Results show enhanced performance under distributional shifts.

Abstract

Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of clinical NLP. However, these approaches require sufficiently large datasets for training, and trained models have been shown to transfer poorly across sites. These issues have led to the promotion of data collection and integration across different institutions for accurate and portable models. However, this can introduce a form of bias called confounding by provenance. When source-specific data distributions differ at deployment, this may harm model performance. To address this issue, we evaluate the utility of backdoor adjustment for text classification in a multi-site dataset of clinical notes annotated for mentions of substance abuse. Using an evaluation framework devised to measure robustness to distributional shifts, we assess the utility of backdoor adjustment. Our results indicate that backdoor adjustment can effectively mitigate for confounding shift.