Analyzing Patient Feedback Data with Topic Modeling

Jasper Arendsen, Emil Rijcken*, Kalliopi Zervanou, Kim Rietjens, Femke Vlems, Uzay Kaymak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Patient feedback is an increasingly important measure to support quality improvement within healthcare organisations. Until recently, the focus has been on developing mechanisms for collecting patient feedback. However, research into analysis techniques to examine such feedback, especially free-text comments, is limited. The analysis of free-text data requires substantial effort because of the unstructured nature of the responses. As a result, this type of data is often underutilised within healthcare organisations while it contains the most valuable information. This research aims to analyse unstructured patient feedback, collected via a PREM questionnaire, utilising text mining. In particular, the extent to which topics can be extracted from this data is explored. Multiple topic modelling algorithms (LDA, FLSA, FLSA-W, NMF, BTM) are selected based on previous research and the data set characteristics. The applied topic modelling techniques proved to be able to provide a high-level overview of patient experiences. Hence, this research can be considered as one of the first steps towards automated analysis of unstructured patient feedback.

Original languageEnglish
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings
EditorsDavide Ciucci, Inés Couso, Jesús Medina, Dominik Ślęzak, Davide Petturiti, Bernadette Bouchon-Meunier, Ronald R. Yager
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031089732
Publication statusPublished - 2022
Event19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022 - Milan, Italy
Duration: 11 Jul 202215 Jul 2022

Publication series

NameCommunications in Computer and Information Science
Volume1602 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022


  • Fuzzy topic models
  • Information extraction
  • Patient feedback
  • Text mining
  • Topic modeling


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