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A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning

A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning
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Author(s): Kevin Yi-Lwern Yap (National University of Singapore, Singapore)
Copyright: 2014
Pages: 19
Source title: Handbook of Research on Patient Safety and Quality Care through Health Informatics
Source Author(s)/Editor(s): Vaughan Michell (University of Reading, UK), Deborah J. Rosenorn-Lanng (Royal Berkshire Hospital Foundation Trust Reading, UK), Stephen R. Gulliver (University of Reading, UK) and Wendy Currie (Audencia, Ecole de Management, Nantes, France)
DOI: 10.4018/978-1-4666-4546-2.ch010

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Abstract

Pharmaco-cybernetics is an upcoming interdisciplinary field that supports our use of medicines and drugs through the combined use of computational technologies and techniques with human-computer-environment interactions to reduce or prevent drug-related problems. The advent of pharmaco-cybernetics has led to the development of various software, tools, and Internet applications that can be used by healthcare practitioners to deliver optimum pharmaceutical care and health-related outcomes. Patients are becoming more informed through health information on the Internet, which empowers them to better participate in the management of their own conditions. Focusing on patients with cancer, this chapter describes the use of a pharmaco-cybernetics approach to identify clinically relevant predictors of two debilitating adverse drug reactions, which are a cause of patient safety – chemotherapy-induced nausea and vomiting and febrile neutropenia. The early identification of such clinical predictors enables clinicians to prevent or reduce the occurrence of adverse drug reactions in cancer patients undergoing chemotherapy through appropriate management strategies. The computational methods used in this approach involve two unsupervised machine-learning techniques – principal component and multiple correspondence analyses. Using two case examples, this chapter shows the potential of machine-learning techniques for identifying patients who are at greater risks of these adverse drug reactions, thus enhancing patient safety. This chapter also aims to increase the awareness among healthcare professionals and clinician-scientists about the usefulness of such techniques in clinical patient populations, so that these can be considered as part of clinical care pathways to enhance patient safety and effectively manage cancer patients on chemotherapy.

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