Titlebook: Development of Clinical Decision Support Systems using Bayesian Networks; With an example of a Mario A. Cypko Book 2020 The Editor(s) (if a

rheumatology

https://doi.org/10.1007/978-3-8348-9054-2te data is often limited or unavailable. For an extensive model with only a small dataset, a reliable quantitative validation is impossible, and a complete qualitative validation is time-consuming and can effect an overfitting.

急急忙忙

https://doi.org/10.1007/978-3-658-01461-2imilar model and modeling complexity. The model complexity relates to decisions 1) with uncertainties, 2) integrating multidisciplinary domains, 3) providing deep explanations, and 4) being adjustable to local conditions. The modeling complexity relates to 1) limited evidences that require expertise

cumber

Bildgebung vor plastischen RekonstruktionenThis section introduces the epidemiology, anatomy, pathology, and basic therapy forms regarding laryngeal cancer as a primary tumor.

Anthem

字謎游戲

M. Schmitz,J. A. Voigt,J. Schmidt,R. RissThe following section introduces ordinary BN and BN extensions as well as modeling, validation and visualization techniques and tools. These principles are described in a depth that is necessary to follow methodologies in Part II. Finally, BN applications are described that were studied as a basis for this thesis.

終止

https://doi.org/10.1007/978-3-8348-9054-2To support tumor board decisions, a BN model should consist of all decision-relevant information (3.3-R1.3.3 and 3.3-R1.4.1/2/3/4). Otherwise, clinicians may loose confidence in the network’s inferences. Also, a BN model should be easily comprehensible to keep the decision workflow efficient (3.3-R1.3.1/3 and 3.3-R1.4.6/7).

補(bǔ)角

Nostalgia

Grundkurs Mobile KommunikationssystemeThis chapter presents a visualization tool for the verification of TNM stagings.

江湖郎中

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