Published on in Vol 5, No 3 (2017): Jul-Sept

Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers

Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers

Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers

Journals

  1. Spasic I, Nenadic G. Clinical Text Data in Machine Learning: Systematic Review. JMIR Medical Informatics 2020;8(3):e17984 View
  2. van Dael J, Reader T, Gillespie A, Neves A, Darzi A, Mayer E. Learning from complaints in healthcare: a realist review of academic literature, policy evidence and front-line insights. BMJ Quality & Safety 2020;29(8):684 View
  3. Liu Y, Wan Y, Su X. Identifying individual expectations in service recovery through natural language processing and machine learning. Expert Systems with Applications 2019;131:288 View
  4. Anagnostou P, Tasoulis S, Vrahatis A, Georgakopoulos S, Prina M, Ayuso-Mateos J, Bickenbach J, Bayes-Marin I, Caballero F, Egea-Cortés L, García-Esquinas E, Leonardi M, Scherbov S, Tamosiunas A, Galas A, Haro J, Sanchez-Niubo A, Plagianakos V, Panagiotakos D. Enhancing the Human Health Status Prediction: The ATHLOS Project. Applied Artificial Intelligence 2021;35(11):834 View
  5. Ma S, Jiang S, Yang O, Zhang X, Fu Y, Zhang Y, Kaareen A, Ling M, Chen J, Shang C. Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation. JMIR Formative Research 2024;8:e49031 View

Books/Policy Documents

  1. Xia C, Zhao D, Wang J, Liu J, Ma J. Smart Health. View
  2. Galitsky B. Artificial Intelligence for Healthcare Applications and Management. View