Personalised stage-specific simulation models

This objective is our core objective as it encompasses the development of the predictive models for each of the four clinical phases of stroke, i.e. prevention, treatment, rehabilitation and reintegration. Our models will be based on the harmonized data sources from objective 1 and the input/output definitions from objective 2. A major focus of this objective is hybrid modelling for the first two clinical stages of stroke. Hybrid modelling is the combined use of mechanistic and phenomenological models. Hybrid modelling combines the best of two worlds: i) the simulation-based possibilities of mechanistic models with ii) the predictive versatility of machine-learning models. In the stroke prevention stage the PRECISE4Q model will predict the evolution of key patient features over in response to different treatments, such as diet, exercise, and medication in a hybrid modelling approach. It will predict the overall risk of stroke for a patient participating in a treatment plan. Using the outputs of this model a healthcare professional will be able to develop and compare personalised intervention regimes for their patients to increase the patient’s resilience to stroke. In a hybrid modelling approach the outputs of an existing biophysiological mechanistic imaging-based model of brain blood flow and perfusion will be fused with patient-specific clinical data to predict a modified Rankin Scale for the patient 3 months post-stroke. The second model will integrate a range of heterogeneous patient-specific data (e.g. lab, OMICs etc.), information extracted from the textual records, and the modified Rankin Scale estimated by the other model. The output of this second model will be a complex quality of life (QoL) profile for the patient, including QoL indicators such the probability of the patient returning to work. For the rehabilitation stage PRECISE4Q will create a model that will generate a patient specific daily schedule of rehabilitation activities annotated with the expected performance level of the activity along with the expected cognitive and functional profile of the patient at the end of the rehabilitation programme. The output of the model will support doctors in designing personalised rehabilitation plans for each patient. The PRECISE4Q model supporting patient treatment during reintegration phase will predict a long-term (1-year post-stroke) complex QoL profile for a patient. The output of the model will inform the design of individualised support plans to long-term resilience and quality of life.