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What is Unsupervised Machine Learning

Handbook of Research on Patient Safety and Quality Care through Health Informatics
A computational method that typically involves a model that is generated to fit observations. Unlike supervised learning, there is no a priori output, therefore characteristics about the data set can be described from the observations without any predefined target.
Published in Chapter:
A Pharmaco-Cybernetics Approach to Patient Safety: Identifying Adverse Drug Reactions through Unsupervised Machine Learning
Kevin Yi-Lwern Yap (National University of Singapore, Singapore)
DOI: 10.4018/978-1-4666-4546-2.ch010
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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Building a Chatbot for Libraries
The machine uses artificial intelligence algorithms to cluster unlabeled datasets.
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Explainable Safety Risk Management in Construction With Unsupervised Learning
Training ML algorithms without labeled input data through an understanding of the patterns within the dataset.
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Quality Improvement of Healthcare Services Through Data Analytics Processes
Unlike supervised machine learning where the input data includes a target variable, unsupervised machine learning algorithms are not provided with any target variable. Instead, Unsupervised Machine Learning refers to methods to discover patterns, relationships, and structures in the data set without any prior knowledge or guidance. Association and cluster analysis are examples of Unsupervised Machine Learning.
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Examining the Evolution of E-Government Development of Nations Through Machine Learning Techniques
The use of machine learning techniques to identify or find hidden patterns or structures without providing output examples. The algorithms discover hidden patterns or data groupings without the need for human intervention. Clustering is one such example.
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