AI-guided solutions for early detection of dementia
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Zoe Kourtzi
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Alzheimer’s disease (AD) is characterised by a dynamic process of neurocognitive changes from normal cognition to mild cognitive impairment (MCI) and progression to dementia. However, not all individuals with MCI develop dementia. Predicting whether individuals with MCI will decline (i.e. progressive MCI) or remain stable (i.e. stable MCI) is impeded by patient heterogeneity due to comorbidities that may lead to MCI diagnosis without progression to AD. Despite the importance of early diagnosis of AD for prognosis and personalised interventions, we still lack robust tools for predicting individual progression to dementia. Here, we propose a novel trajectory modelling approach based on metric learning that mines multimodal data from MCI patients to derive individualised prognostic scores of cognitive decline due to AD. Our approach affords the generation of a predictive and interpretable marker of individual variability in progression to dementia due to AD based on cognitive data alone. Including non-invasively measured biological data (grey matter density, APOE 4) enhances predictive power and clinical relevance. Our trajectory modelling approach has strong potential to facilitate effective stratification of individuals based on prognostic disease trajectories, reducing MCI patient misclassification with important implications for clinical practice and discovery of personalised interventions.
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The cooked and the raw; extracting and exploring structured and unstructured clinical data from patient electronic health records
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Paul Schofield
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Electronic health records (EHRs) contain information critical to the realisation of the promise of personalised medicine, but also data essential for the discovery of the molecular basis of disease. Clinical information systems and EHRs were not developed for the discovery, integration and export of information, most being based on the concept of paper records going back to the 1990s. Consequently we find in EHRs information contained in administrative, diagnostic and procedure codes, which are highly structured and standardised ( pre-cooked) , the results of investigative tests, ranging from blood chemistry to images, which might be regarded as partially structured information (lukewarm?), and finally narrative reports of clinical encounters and discharge letters which are rich sources of information but completely unstructured – raw data. Reliably extracting and integrating these types of information is a huge challenge, but the ability to retrieve coded and quantitative data into a common symbolic framework opens up the possibility of connecting these data together with the large amounts of background knowledge now available, to begin to make semantic sense of our whole ‘menu’.
I will discuss three approaches to extracting and using EHR information: the first uses the Komenti platform which is designed to extract information from free text into semantically formalised ontological annotations, the second is an approach to combine quantitative data into that same semantic framework. The third, a new resource, axiomatises ICD-10 terms uses the Human phenotype ontology for integration with existing knowledge and, for example, patient classification. The promise of these multi-pronged approaches will be discussed.
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