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Primary healthcare computerized medical records (CMR) are a powerful source of information as they contain population level health indicators. These data can be used for estimations of disease incidence, provide insight into disease complexity and identify sub-groups of patients, among other things. National and regional level data aid decision-making in response to potential disease outbreaks, while identification of patient sub-groups can aid treatment planning, moving towards personalised medicine.
Despite the enormous potential, identifying trends in large primary care data and inferring meaning from these data is extremely challenging due to their complexity, heterogeneity, dimensionality, incompleteness and noisiness. CMR data are often mixed-type, making traditional data analysis tools unavailable. At NPL we have developed a generic data pre-processing and deep learning approach for visualization and analysis of CMR data. Our tools enable the analysis of CMR data, as well as other related data types, such as demographics, metadata, medical histories, in a way that identifies non-linear patterns in an unlabelled manner. The features that form patient clusters can be linked back to the input data and interpreted by the clinician or stakeholder to aid in their decision making in complex healthcare scenarios. This framework can also be applied as a data exploration study in order to obtain data-driven hypotheses that can be tested with further data.
This work is ongoing and we are keen to work with as many people as possible. Please contact us if you would like to know more or want to discuss the challenges in your work environment.
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