Predictive modeling of clinical trial terminations using feature engineering and embedding learning

Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be termin...

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Autores principales:	Magdalyn E. Elkin, Xingquan Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6f7430ca29734e37a28ff3887950ba2e
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spelling	oai:doaj.org-article:6f7430ca29734e37a28ff3887950ba2e2021-12-02T14:26:48ZPredictive modeling of clinical trial terminations using feature engineering and embedding learning10.1038/s41598-021-82840-x2045-2322https://doaj.org/article/6f7430ca29734e37a28ff3887950ba2e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82840-xhttps://doaj.org/toc/2045-2322Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc., are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study.Magdalyn E. ElkinXingquan ZhuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Magdalyn E. Elkin Xingquan Zhu Predictive modeling of clinical trial terminations using feature engineering and embedding learning
description	Abstract In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc., are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study.
format	article
author	Magdalyn E. Elkin Xingquan Zhu
author_facet	Magdalyn E. Elkin Xingquan Zhu
author_sort	Magdalyn E. Elkin
title	Predictive modeling of clinical trial terminations using feature engineering and embedding learning
title_short	Predictive modeling of clinical trial terminations using feature engineering and embedding learning
title_full	Predictive modeling of clinical trial terminations using feature engineering and embedding learning
title_fullStr	Predictive modeling of clinical trial terminations using feature engineering and embedding learning
title_full_unstemmed	Predictive modeling of clinical trial terminations using feature engineering and embedding learning
title_sort	predictive modeling of clinical trial terminations using feature engineering and embedding learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6f7430ca29734e37a28ff3887950ba2e
work_keys_str_mv	AT magdalyneelkin predictivemodelingofclinicaltrialterminationsusingfeatureengineeringandembeddinglearning AT xingquanzhu predictivemodelingofclinicaltrialterminationsusingfeatureengineeringandembeddinglearning
_version_	1718391316966014976

Predictive modeling of clinical trial terminations using feature engineering and embedding learning

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