Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures

TL;DR

This paper explores weakly supervised text classification methods to analyze free text comments in patient-reported outcome measures, aiming to identify health-related themes with limited labeled data.

Contribution

It applies five WSTC techniques to PROMs comments, demonstrating their potential and limitations in classifying health-related themes with minimal supervision.

Findings

Moderate classification performance achieved.

Keyword-based WSTC shows potential with limited labels.

Performance varies across different health themes.

Abstract

Free text comments (FTC) in patient-reported outcome measures (PROMs) data are typically analysed using manual methods, such as content analysis, which is labour-intensive and time-consuming. Machine learning analysis methods are largely unsupervised, necessitating post-analysis interpretation. Weakly supervised text classification (WSTC) can be a valuable method of analysis to classify domain-specific text data in which there is limited labelled data. In this paper, we apply five WSTC techniques to FTC in PROMs data to identify health-related quality of life (HRQoL) themes reported by colorectal cancer patients. The WSTC methods label all the themes mentioned in the FTC. The results showed moderate performance on the PROMs data, mainly due to the precision of the models, and variation between themes. Evaluation of the classification performance illustrated the potential and limitations of keyword based WSTC to label PROMs FTC when labelled data is limited.