Predicting adherence of patients with HF through machine learning techniques

Healthcare Technology Letters

article

Georgia Spiridon Karanasiou¹  Evanthia Eleftherios Tripoliti¹  Theofilos Grigorios Papadopoulos²  Fanis Georgios Kalatzis¹  Yorgos Goletsis³  Katerina Kyriakos Naka⁴  Aris Bechlioulis⁴  Abdelhamid Errachid⁶  Dimitrios Ioannis Fotiadis¹ 

[1] Department of Biomedical Research, Institute of Molecular Biology and Biotechnology;Unit of Medical Technology and Intelligent Information Systems, University of Ioannina;Department of Economics, University of Ioannina;Michaelidion Cardiac Center, University of Ioannina;Department of Cardiology, University of Ioannina;Université de Lyon, Institut de Sciences Analytiques

关键词: patient treatment;    patient monitoring;    learning (artificial intelligence);    cardiology;    diseases;    patient adherence prediction;    heart failure;    machine learning techniques;    chronic disease;    patient monitoring;    medication;    nutrition;    physical activity;   

DOI  :  10.1049/htl.2016.0041

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.

【 授权许可】

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