Length of hospital stay prediction with an integrated approach of statistical-based fuzzy cognitive maps and artificial neural networks

DOĞU E., ALBAYRAK Y. E., Tuncay E.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, cilt.59, sa.3, ss.483-496, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59 Sayı: 3
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s11517-021-02327-9
  • Dergi Adı: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, CINAHL, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Sayfa Sayıları: ss.483-496
  • Anahtar Kelimeler: Medical decision-making, Length of hospital stay, COPD, Statistical-based fuzzy cognitive maps, Artificial neural networks
  • Galatasaray Üniversitesi Adresli: Evet

Özet

Chronic obstructive pulmonary disease (COPD) is a global burden, which is estimated to be the third leading cause of death worldwide by 2030. The economic burden of COPD grows continuously because it is not a curable disease. These conditions make COPD an important research field of artificial intelligence (AI) techniques in medicine. In this study, an integrated approach of the statistical-based fuzzy cognitive maps (SBFCM) and artificial neural networks (ANN) is proposed for predicting length of hospital stay of patients with COPD, who admitted to the hospital with an acute exacerbation. The SBFCM method is developed to determine the input variables of the ANN model. The SBFCM conducts statistical analysis to prepare preliminary information for the experts and then collects expert opinions accordingly, to define a conceptual map of the system. The integration of SBFCM and ANN methods provides both statistical data and expert opinion in the prediction model. In the numerical application, the proposed approach outperformed the conventional approach and other machine learning algorithms with 79.95% accuracy, revealing the power of expert opinion involvement in medical decisions. A medical decision support framework is constructed for better prediction of length of hospital stay and more effective hospital management.