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PRONIA

Workpackage 6: Multimodal Data Fusion

Workpackage leader

Stephen Wood

Objectives of the Workpackage

The primary objective of Workpackage 6 (WP 6) is to optimise the predictive power of the different types of data collected by PRONIA (clinical, neurocognitive, MRI and genetic data) by pooling them together. Using this pooled ‘multi-modal’ data, WP 6 will create models which will predict prognostic outcomes significantly better than models, which employ only one type of data.

Description of the tasks

WP 6 is led by the undefinedUniversity of Birmingham and is responsible for systematically investigating whether the different combinations of data channels collected by PRONIA improve accuracy and reliability of prediction in the individual help-seeker. Where different types of data do supplement each other in this way, this knowledge will be used to improve prediction beyond the level achieved by any of these sources alone. As each different type of data has different weaknesses in their ability to accurately predict outcome, by combining the different data types the overall prediction error rate of prognosis can be reduced. In summary, WP 6 will boost PRONIA’s prognostic performance and clinical applicability by fusing together these different types of information, thus producing ‘multimodal’ markers which optimally adapt to the diagnostic needs of the given clinical situation.

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