Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Identifying service gaps from public patient opinions through text mining

Tang, Min, Liu, Yiping, Li, Zhiguo and Liu, Ying 2018. Identifying service gaps from public patient opinions through text mining. Presented at: ICSEE 2018: International Conference on Intelligent Computing for Sustainable Energy and Environment and IMIOT 2018: International Conference on Intelligent Manufacturing and Internet of Things, Chongqing, China, 21-23 September 2018. Published in: Li, Kang, Fei, Minrui, Du, Dajan, Yang, Zhile and Yang, Dongsheng eds. Intelligent Computing and Internet of Things: First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Chongqing, China,. Communications in Computer and Information Science Singapore: Springer, pp. 99-108. 10.1007/978-981-13-2384-3_10

Full text not available from this repository.

Abstract

Nowadays, healthcare systems have become increasingly patient-centered and the unstructured, open-ended and patient-driven feedback has drawn a significant attention from medical and healthcare organizations. Based on this, we are motivated to harness various machine learning algorithms to process such a large amount of unstructured comments posted on public patient opinion sites. We first used sentiment analysis to automatically predict the concerns of patients from the training set which was already labelled. Then, with the help of the clustering, we extracted the hot topics related to a specific domain to reflect the service issues that patients concern most. Through experimental studies, the performance of different algorithms and the influence of different parameter were compared. Finally, refering to the survey and previous studies, the results were analyzed to obtain the conclusions.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Springer
ISBN: 978-981-13-2384-3
ISSN: 1865-0929
Last Modified: 27 Aug 2019 14:43
URI: http://orca.cf.ac.uk/id/eprint/122114

Actions (repository staff only)

Edit Item Edit Item