Using predictive analytics to identify drug-resistant epilepsy patients

Delen D., Davazdahemami B., Eryarsoy E., Tomak L., Valluru A.

HEALTH INFORMATICS JOURNAL, cilt.26, sa.1, ss.449-460, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1177/1460458219833120
  • Dergi Adı: HEALTH INFORMATICS JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, CINAHL, Computer & Applied Sciences, EBSCO Education Source, Educational research abstracts (ERA), EMBASE, INSPEC, Library and Information Science Abstracts, MEDLINE, Library, Information Science & Technology Abstracts (LISTA)
  • Sayfa Sayıları: ss.449-460
  • Anahtar Kelimeler: anti-epileptic drugs, drug resistance, epilepsy, machine learning, predictive analytics, refractory epilepsy, QUALITY STANDARDS SUBCOMMITTEE, INTERNATIONAL-LEAGUE, AMERICAN ACADEMY, CLASSIFICATION, IDENTIFICATION, NEUROLOGY, SEIZURES, HEALTH, COST, EEG
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

Epilepsy is one of the most common brain disorders that greatly affects patients' quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight the potential use of machine-learning techniques in facilitating medical decisions and suggest the possibility of extending the proposed approach for developing a clinical decision support system for medical professionals.