Weakly Supervised Text Classification on Free Text Comments in
  Patient-Reported Outcome Measures
Anna-Grace Linton Vania Dimitrova Amy Downing Richard Wagland Adam Glaser
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.