Literature DB >> 31156111

Using Machine Learning for Personalized Patient Adherence Level Determination.

Maksim Taranik1, Georgy Kopanitsa2.   

Abstract

The paper deals with using a machine-learning algorithm for patient adherence level determination. For this purpose, we developed a neural network using the Python language, Keras library, and PyCharm platform. We analyzed different medical data collected from medical staff, patient interviews, and measurements preprocessed using a fuzzy Mamdani algorithm. After analysing 369 records we received 79.4% of accuracy.

Entities:  

Keywords:  Adherence; Keras; fuzzy logic; machine learning

Mesh：

Year:  2019        PMID： 31156111

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN： 0926-9630


  2 in total

Review 1.  Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Authors:  Mary Anne Schultz; Rachel Lane Walden; Kenrick Cato; Cynthia Peltier Coviak; Christopher Cruz; Fabio D'Agostino; Brian J Douthit; Thompson Forbes; Grace Gao; Mikyoung Angela Lee; Deborah Lekan; Ann Wieben; Alvin D Jeffery
Journal:  Comput Inform Nurs       Date:  2021-05-06       Impact factor: 1.985

2.  Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms.

Authors:  Xing-Wei Wu; Heng-Bo Yang; Rong Yuan; En-Wu Long; Rong-Sheng Tong
Journal:  BMJ Open Diabetes Res Care       Date:  2020-03
  2 in total

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