Analyzing Patient Feedback Data with Topic Modeling

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

doi:10.1007/978-3-031-08974-9_20

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 proceeding › Conference contribution › Academic › peer-review

Abstract

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 language	English
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings
Editors	Davide Ciucci, Inés Couso, Jesús Medina, Dominik Ślęzak, Davide Petturiti, Bernadette Bouchon-Meunier, Ronald R. Yager
Place of Publication	Cham
Publisher	Springer
Pages	248-258
Number of pages	11
ISBN (Print)	9783031089732
DOIs	https://doi.org/10.1007/978-3-031-08974-9_20
Publication status	Published - 2022
Externally published	Yes
Event	19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022 - Milan, Italy Duration: 11 Jul 2022 → 15 Jul 2022

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1602
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022
Country/Territory	Italy
City	Milan
Period	11/07/22 → 15/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Fuzzy topic models
Information extraction
Patient feedback
Text mining
Topic modeling

Access to Document

10.1007/978-3-031-08974-9_20

Cite this

Arendsen, J., Rijcken, E., Zervanou, K., Rietjens, K., Vlems, F., & Kaymak, U. (2022). Analyzing Patient Feedback Data with Topic Modeling. In D. Ciucci, I. Couso, J. Medina, D. Ślęzak, D. Petturiti, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings (pp. 248-258). (Communications in Computer and Information Science; Vol. 1602 ). Springer. https://doi.org/10.1007/978-3-031-08974-9_20

Arendsen, Jasper ; Rijcken, Emil ; Zervanou, Kalliopi et al. / Analyzing Patient Feedback Data with Topic Modeling. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings. editor / Davide Ciucci ; Inés Couso ; Jesús Medina ; Dominik Ślęzak ; Davide Petturiti ; Bernadette Bouchon-Meunier ; Ronald R. Yager. Cham : Springer, 2022. pp. 248-258 (Communications in Computer and Information Science).

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abstract = "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.",

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author = "Jasper Arendsen and Emil Rijcken and Kalliopi Zervanou and Kim Rietjens and Femke Vlems and Uzay Kaymak",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022 ; Conference date: 11-07-2022 Through 15-07-2022",

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Arendsen, J, Rijcken, E, Zervanou, K, Rietjens, K, Vlems, F & Kaymak, U 2022, Analyzing Patient Feedback Data with Topic Modeling. in D Ciucci, I Couso, J Medina, D Ślęzak, D Petturiti, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings. Communications in Computer and Information Science, vol. 1602 , Springer, Cham, pp. 248-258, 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022, Milan, Italy, 11/07/22. https://doi.org/10.1007/978-3-031-08974-9_20

Analyzing Patient Feedback Data with Topic Modeling. / Arendsen, Jasper; Rijcken, Emil; Zervanou, Kalliopi et al.
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings. ed. / Davide Ciucci; Inés Couso; Jesús Medina; Dominik Ślęzak; Davide Petturiti; Bernadette Bouchon-Meunier; Ronald R. Yager. Cham: Springer, 2022. p. 248-258 (Communications in Computer and Information Science; Vol. 1602 ).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Kaymak, Uzay

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AB - 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.

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Arendsen J, Rijcken E, Zervanou K, Rietjens K, Vlems F, Kaymak U. Analyzing Patient Feedback Data with Topic Modeling. In Ciucci D, Couso I, Medina J, Ślęzak D, Petturiti D, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings. Cham: Springer. 2022. p. 248-258. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-08974-9_20