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Computational modelling in healthcare: Making confident predictions in a world of error and uncertainty

Computational modelling in healthcare: Making confident predictions in a world of error and uncertainty

The Gannochy Room

Wolfson Medical Building

University of Glasgow

Tuesday 26th April, 9am-5:30pm

Healthcare technologies have advanced rapidly in recent years, largely thanks to the multidisciplinary approaches that are increasingly being adopted.   In particular, mathematical and computational modelling techniques are being utilized to provide additional insight that cannot be obtained from experiments alone, to help reduce the number of costly experiments which often involve animals, to provide a systematic approach to medical device design and clinical interventions and to meet the ever more stringent demands of regulatory bodies.

As we move towards a bright future of computational modelling at the forefront of patient-specific healthcare, we must pause and consider the validity of our models.  Computational modelling is fraught with error and uncertainty.  Whether it be in estimating the many parameter values in our increasingly complex models, in segmenting complex geometries from medical images, in making modeling assumptions to allow for timely computation, or in handling patient-to-patient variability, the issue of error and uncertainty requires serious consideration.  And with a whole host of computational software and image reconstruction packages being used, many of which are black boxes, a believable and colourful simulation can often mask serious modelling errors.  Taking all this into account, how confident can we be with our model predictions?

This one day workshop aims to bring together academics, clinicians and industry representatives to stimulate thinking around the issue of error and uncertainty in computational modelling applied to healthcare.  We will hear from experts in the field on how we can identify, classify and limit error and uncertainties in various aspects of our computational models so that we can make more confident predictions. 

The workshop will focus on a number of areas including:

·         Parameter estimation techniques

·         Patient-to-patient variability

·         Model reduction strategies

·         Medical image geometry segmentation

·         Statistical analysis of error and uncertainty

·         Success stories of predictive models which have made it into clinical practice