Using Machine Learning for Personalized Patient Adherence Level Determination

Maksim Taranik, Georgy Kopanitsa

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)174-178
Number of pages5
JournalStudies in Health Technology and Informatics
Volume261
Publication statusPublished - 1 Jan 2019
Externally publishedYes

Fingerprint

Patient Compliance
Learning algorithms
Learning systems
Boidae
Neural networks
Medical Staff
Libraries
Language
Interviews
Machine Learning

Keywords

  • Adherence
  • fuzzy logic
  • Keras
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Using Machine Learning for Personalized Patient Adherence Level Determination. / Taranik, Maksim; Kopanitsa, Georgy.

In: Studies in Health Technology and Informatics, Vol. 261, 01.01.2019, p. 174-178.

Research output: Contribution to journalArticle

@article{a35c7f4dc9a14655943c964ca8a83c1e,
title = "Using Machine Learning for Personalized Patient Adherence Level Determination",
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.",
keywords = "Adherence, fuzzy logic, Keras, machine learning",
author = "Maksim Taranik and Georgy Kopanitsa",
year = "2019",
month = "1",
day = "1",
language = "English",
volume = "261",
pages = "174--178",
journal = "Studies in Health Technology and Informatics",
issn = "0926-9630",
publisher = "IOS Press",

}

TY - JOUR

T1 - Using Machine Learning for Personalized Patient Adherence Level Determination

AU - Taranik, Maksim

AU - Kopanitsa, Georgy

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Adherence

KW - fuzzy logic

KW - Keras

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85067130669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85067130669&partnerID=8YFLogxK

M3 - Article

VL - 261

SP - 174

EP - 178

JO - Studies in Health Technology and Informatics

JF - Studies in Health Technology and Informatics

SN - 0926-9630

ER -