Explainable data-driven modeling of patient satisfaction survey data

Ning Liu, Soundar Kumara, Eric Reich

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


In the personalized patient-centered healthcare, self-reported patient satisfaction survey data plays an important role. Given the patient survey data, it is necessary to identify the drivers of patient satisfaction and explain them so that such patterns can be used in future as well as necessary corrective actions can be taken. In healthcare, both accuracy and interpretability are important criteria for choosing a reliable predictive model for analyzing patient data. Usually, complex models such as Random Forest, neural networks can achieve high prediction accuracy but lack necessary interpretation to their prediction results. In this paper, we address this problem by proposing a local explanation method to interpret complex model prediction results. First, we build a predictive model using Random Forest to fit the patient satisfaction data. Second, we utilize local explanation method to provide insights into the Random Forest prediction results so as to discover true reasons behind patient experiences and overall ratings. Specifically, our approach allows us to interpret patient's overall rating of a hospital at the individual level, and find out the set of the most influential factors for each patient. We focus on all unhappy patients to investigate the top reasons for patient dissatisfaction. Our approach and findings will help to establish guidelines for a quality healthcare.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781538627143
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017


Other5th IEEE International Conference on Big Data, Big Data 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization


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