Abstract
Successful application of machine learning in healthcare requires accuracy, transparency, acceptability, ability to deal with complex data, ability to deal with background knowledge, efficiency, and exportability. Rule learning is known to satisfy the above criteria. This chapter introduces rule learning in healthcare, presents very expressive attributional rules, briefly describes the AQ21 rule learning system, and discusses three application areas in healthcare and health services research.
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Wojtusiak, J. (2014). Rule Learning in Healthcare and Health Services Research. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_7
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DOI: https://doi.org/10.1007/978-3-642-40017-9_7
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