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Data Integration & Homogenization through an ontology

Data Integration & Homogenization through an ontology

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In the current study, a model-based system for predicting resilience in silico, as part of personalizing precision medicine, to better understand the needs for improved therapeutic protocols of each patient is proposed. The computational environment, which is currently under implementation within the BOUNCE EU project (“Predicting Effective Adaptat...

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... model [15] [16]. The process is a time-consuming and iterative process as trying to produce the mappings the semantic model might have to be updated, which might also lead to updating individual mappings. Those mappings provide declarative correspondences between the data columns and specific ontology terms. This iterative process is shown in Fig. 4. When those mappings are available, the data integration engine can either transform, semantically uplift and store them in a central data warehouse or enable federated access to the data where they reside. The former approach has benefits in terms of efficiency but has to deal with outdated data, whereas the latter enables access ...

Citations

... All aforementioned data are collected using standardized questionnaires and are available through the Noona 4 tool. Then data are exported in batches and are continuously integrated using a novel architecture [12] and data infrastructure [13] combining a data lake for staging the available information and an ontology for the integration and their harmonization and a research supporting tool for facilitating data exploration and visualization [14], [15]. ...
Conference Paper
Breast cancer diagnosis has been associated with poor mental health, with significant impairment of quality of life. In order to ensure support for successful adaptation to this illness, it is of paramount importance to identify the most prominent factors affecting well-being that allow for accurate prediction of mental health status across time. Here we exploit a rich set of clinical, psychological, socio-demographic and lifestyle data from a large multicentre study of patients recently diagnosed with breast cancer, in order to classify patients based on their mental health status and further identify potential predictors of such status. For this purpose, a supervised learning pipeline using cross-sectional data was implemented for the formulation of a classification scheme of mental health status 6 months after diagnosis. Model performance in terms of AUC ranged from 0.81± 0.04 to 0.90± 0.03. Several psychological variables, including initial levels of anxiety and depression, emerged as highly predictive of short-term mental health status of women diagnosed with breast cancer.