Titlebook: Advanced Data Analytics in Health; Philippe J. Giabbanelli,Vijay K. Mago,Elpiniki I. Book 2018 Springer International Publishing AG, part

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https://doi.org/10.1007/978-3-0348-6580-7es, e.g. cigarettes’ effect on lung cancer. However, for conditions that are multifactorial or for which multiple variables interact to affect risk, these approaches have been less successful. Machine learning approaches such as classifiers can improve risk prediction due to their ability to empiric

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https://doi.org/10.1007/978-3-662-63028-0ts evolutionary-based learning capabilities is presented for classifying mammography images. The main contribution of this work is two-fold: (a) to propose a new segmentation approach called the threshold based region growing (TBRG) algorithm for segmentation of mammography images, and (b) to implem

ALIAS

Andreas Eisele,Simon Sagmeistery delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness

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Medicare

https://doi.org/10.1007/978-3-7091-4174-8diabetes-related service types subject to a copayment waiver: laboratory tests, physician office visits, diabetes supplies, diabetes medications, antihypertensive (blood pressure) medications, and cholesterol-lowering medications. We evaluated the impact of this natural experiment compared to a matc

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Zusammenfassung aller Versuchsergebnisse,obesity being the result of a complex system. This complexity is often ignored by generic interventions, and not fully utilized for clinical decision-making. We focused on heterogeneity and feedback loops as key parts of this complexity. We measured heterogeneity and found it high, in a demographica

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978-3-030-08571-1Springer International Publishing AG, part of Springer Nature 2018