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Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery

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Clinical Research Informatics

Part of the book series: Health Informatics ((HI))

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Abstract

Clinical information, stored over time and increasingly linked to other types of information such as environmental and social determinants of health and healthcare claims, is a potentially rich data source for clinical research. Knowledge discovery in databases (KDD) is a process for pattern discovery and predictive modeling in large databases. KDD encompasses and makes extensive use of data-mining methods—automated processes and algorithms that enable pattern recognition and classification. Characteristically, KDD involves the use of machine learning methods developed in the domain of artificial intelligence and information retrieval. These methods, which include both structure learning and parameter learning, have been applied to healthcare and biomedical data for various purposes with good success and potential or realized clinical translation. We introduce the Fayyad model of knowledge discovery in databases and describe the steps of the process, providing select examples from clinical research informatics. These steps range from initial data selection and preparation to interpretation and evaluation. Commonly used data-mining methods are surveyed: artificial neural networks, decision-tree induction, support vector machines (kernel methods), association-rule induction, k-nearest neighbor, and probabilistic methods such as Bayesian networks. We link methods for evaluating the models that result from the KDD process to methods used in diagnostic medicine, spotlighting measures derived from a confusion matrix and receiver operating characteristic curve analysis and, more recently, uncertainty quantification and conformal prediction. Throughout the chapter, we discuss salient aspects of biomedical data management and use, including applications, the use of FAIR principles, pipelines and infrastructure for KDD, and future directions.

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Authors and Affiliations

  1. College of Nursing and Department of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA

    Mollie R. Cummins

  2. Department of Biomedical Informatics, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA

    Senthil K. Nachimuthu, Samir E. Abdelrahman, Julio C. Facelli & Ramkiran Gouripeddi

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  1. Mollie R. Cummins
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  2. Senthil K. Nachimuthu
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  3. Samir E. Abdelrahman
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  4. Julio C. Facelli
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  5. Ramkiran Gouripeddi
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Correspondence to Mollie R. Cummins .

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Editors and Affiliations

  1. Learning Health Sciences, University of Michigan School of Medicin, Ann Arbor, MI, USA

    Rachel L. Richesson

  2. School of Information, University of South Florida, Tampa, FL, USA

    James E. Andrews

  3. Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA

    Kate Fultz Hollis

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Cummins, M.R., Nachimuthu, S.K., Abdelrahman, S.E., Facelli, J.C., Gouripeddi, R. (2023). Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery. In: Richesson, R.L., Andrews, J.E., Fultz Hollis, K. (eds) Clinical Research Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-031-27173-1_20

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