Titlebook: Measurement and Analysis in Transforming Healthcare Delivery; Volume 1: Quantitati Peter J. Fabri Book 2016 Springer International Publishi

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Software for Analyticsomputers have made the computation straightforward. However, knowing “what tests to use,” “how to use them,” and “how to interpret them” still requires an intensive understanding. This chapter addresses the tools available in modern software to accomplish visualization and computation, focusing prim

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Measurement and Uncertaintyity of data, distributions of data, the progression of data into information, then knowledge, then wisdom, and statistical error. Data are derived from existing databases and extracted into spreadsheets which contain datasets. Most statistical software is designed to manipulate numerical values; “R”

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Mathematical and Statistical Concepts in Data Analysisgebra concepts. Once again, it isn’t necessary to be able to perform the steps manually, but it is essential to understand the concepts in order to implement the computer methods appropriately. Refining the dataset, selecting variables, and correcting non-normality and collinearity then allow develo

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Analysis by Modeling Datay of different methods and combining them as an “ensemble.” Each model can be “measured” for usefulness and validated against an independent dataset. Since datasets are often “unbalanced,” propensity scores and instrumental variables can be used to “approximate” a randomized trial.

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Principles of Supervised Learningised machine learning. Both methods actually have fairly stringent “assumptions,” which are often not met. The advent of high-power computing has allowed the development of newer analytical methods for regression and classification which variably compensate for failed assumptions. Some of these meth

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Unsupervised Machine Learning: Datasets Without Outcomesata. In such cases, . is appropriate. Once again, unsupervised means that there isn’t an outcome to compare the results of a model to. It is tempting to try to “force” a regression or classification model, but often it is quite enlightening to use unsupervised methods to better understand the datase

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Useful Toolsy available in the references and bibliography. Rather, these tools, while uncommonly discussed in the analytics literature, may provide new approaches to some common problems in medical data analysis and interpretation. (1) Outliers are data points that are so “distant” from expected as to suggest

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Measurement and Analysis in Transforming Healthcare DeliveryVolume 1: Quantitati