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Chapter 4 Knowledge Discovery, Data Mining, and Practice-Based Evidence Mollie R. Cummins Ginette A. Pepper Susan D. Horn The next step to comparative effectiveness research is to conduct more prospective large-scale observational cohort studies with the rigor described here for knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) studies. Objectives At the completion of this chapter the reader will be prepared to: 1.Define the goals and processes employed in knowledge discovery and data mining (KDDM) and practice-based evidence (PBE) designs 2.Analyze the strengths and weaknesses of observational designs in general and of KDDM and PBE specifically 3.Identify the roles and activities of the informatics specialist in KDDM and PBE in healthcare environments Key Terms Comparative effectiveness research, 69 Confusion matrix, 62 Data mining, 61 Knowledge discovery and data mining (KDDM), 56 Machine learning, 56 Natural language processing (NLP), 58 Practice-based evidence (PBE), 56 Preprocessing, 56 Abstract The advent of the electronic health record (EHR) and other large electronic datasets has revolutionized efficient access to comprehensive data across large numbers of patients and the concomitant capacity to detect subtle patterns in these data even with missing or less than optimal data quality. This chapter introduces two approaches to knowledge building from clinical data: (1) knowledge discovery and data mining (KDDM) and (2) practice-based evidence (PBE). The use of machine learning methods in retrospective analysis of routinely collected clinical data characterizes KDDM. KDDM enables us to efficiently and effectively analyze large amounts of data and develop clinical knowledge models for decision support. PBE integrates health information technology (health IT) products with cohort identification, prospective data collection, and extensive front-line clinician and patient input for comparative effectiveness research. PBE can uncover best practices and combinations of treatments for specific types of patients while achieving many of the presumed advantages of randomized controlled trials (RCTs). Introduction Leaders need to foster a shared learning culture for improving healthcare. This extends beyond the local department or institution to a value for creating generalizable knowledge to improve care worldwide. Sound, rigorous methods are needed by researchers and health professionals to create this knowledge and address practical questions about risks, benefits, and costs of interventions as they occur in actual clinical practice. Typical questions are as follows: •Are treatments used in daily practice associated with intended outcomes? •Can we predict adverse events in time to prevent or ameliorate them? •What treatments work best for which patients? •With limited financial resources, what are the best interventions to use for specific types of patients? •What types of indi ...
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Clinical Decision Support Systems
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