Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data

Abstract Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indi...

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Autores principales:	Yingqi Gu, Akshay Zalkikar, Mingming Liu, Lara Kelly, Amy Hall, Kieran Daly, Tomas Ward
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ef901330e134f2aaf41a5f98029a866
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spelling	oai:doaj.org-article:4ef901330e134f2aaf41a5f98029a8662021-12-02T18:14:08ZPredicting medication adherence using ensemble learning and deep learning models with large scale healthcare data10.1038/s41598-021-98387-w2045-2322https://doaj.org/article/4ef901330e134f2aaf41a5f98029a8662021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98387-whttps://doaj.org/toc/2045-2322Abstract Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).Yingqi GuAkshay ZalkikarMingming LiuLara KellyAmy HallKieran DalyTomas WardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yingqi Gu Akshay Zalkikar Mingming Liu Lara Kelly Amy Hall Kieran Daly Tomas Ward Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
description	Abstract Clinical studies from WHO have demonstrated that only 50–70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35 % accuracy (Specificity: 78.28 %, Sensitivity: 76.42%, Precision: 77.87%,F1 score: 0.7714, ROC AUC: 0.8390).
format	article
author	Yingqi Gu Akshay Zalkikar Mingming Liu Lara Kelly Amy Hall Kieran Daly Tomas Ward
author_facet	Yingqi Gu Akshay Zalkikar Mingming Liu Lara Kelly Amy Hall Kieran Daly Tomas Ward
author_sort	Yingqi Gu
title	Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
title_short	Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
title_full	Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
title_fullStr	Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
title_full_unstemmed	Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
title_sort	predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4ef901330e134f2aaf41a5f98029a866
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Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data

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