Deliverable

6Clinical Scenarios

The clinical scenarios that are going to be utilised in WP5 are given in the following chapters. In each chapter, the objectives, the steps required and the final results given by the examined scenario are briefly presented in a table format. A detailed presentation of the methodology required to achieve each scenario is also provided along with examples, template figures and tables.

6.1Predictive Modelling Methodologies

The scenarios below highlight the need for a prediction model that given a set of characteristics, predicts in an accurate way the response to a drug X, the toxic effects of an investigational class of drugs and the response/resistance to a specific preoperative drug (i.e. epirubicin). Biomedical data coming from different domains (e.g. microarray, clinical and proteomics) aim to provide enhanced information that leads robust operational performance (i.e. increased confidence, reduced ambiguity and improved classification) enabling evidence based management. Building a prediction model from different data sources is not an easy task. Its architecture is divided in several stages, including:

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  Feature extraction from images.
  
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  Feature selection methods for selecting a subset of relevant features.
  
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  Data integration methods for constructing an informative meta-dataset.
  
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  Building accurate classifiers for the prediction work.
  
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  Pattern recognition methods for estimating the generalization error of the prediction model.
  
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  Statistical methods for evaluating the performance of the prediction model.
  

The following chapters will guide the reader to a brief representation of the previously mentioned techniques before analysing our prediction models for the scenarios described below.