Fuzzy Linguistic Summarization with Genetic Algorithm: An Application with Operational and Financial Healthcare Data

Altintop T., Yager R. R., AKAY D., BORAN F. E., ÜNAL M.

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, cilt.25, sa.4, ss.599-620, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1142/s021848851750026x
  • Dergi Adı: INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.599-620
  • Anahtar Kelimeler: Healthcare management, operational and financial healthcare data, linguistic summarization, fuzzy sets, genetic algorithms, HOSPITAL INFORMATION-SYSTEMS, KNOWLEDGE DISCOVERY, MANAGEMENT, EFFICIENCY, TRENDS, SATISFACTION, DATABASES, FRAMEWORK, MEDICINE, SERVICES
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

It is now well recognized that knowledge extracted from rich healthcare data play a vital role for delivery, management and planning of healthcare services. So far, however, there is not much study done on the domain of operational and financial healthcare data since, up to now, a great deal of works are dedicated to clinical/medical healthcare data for the purposes of diagnosis and treatment of diseases. In this paper, an attempt is made, by applying fuzzy linguistic summarization, for the first time to discover knowledge from operational and financial healthcare data. Fuzzy linguistic summarization, in its simplest term, provides natural language based summaries from a dataset in a human consistent way along with a degree of truth attached to each summary. While basically valuable, its benefit can be increased by only generating summaries with a degree of truth above than an indicated threshold value. A genetic algorithm is developed within this context in order to eliminate less promising and useless linguistic summaries. We assess the proposed approach experimentally on a real data and evaluate the generated summaries to gain actionable insights from them.