Understanding the current situation of the mental health system in a certain country or region is critical to design the appropriate policy and associated intervention plan to be deployed.
This talk shows the application of a new data science process to the identification and characterization of the behaviour of mental health systems in low and middle income countries, as a support for the design of the intervention plans conducted by the World Health Organization to promote the development and improvement of mental healthcare services in those countries.
Rather than developing predictive models, this methodology covers a previous goal of understanding a complex system through descriptive data science tools, thanks to a mixed data- and knowledge-driven approach.
The proposed methodology covers all steps from the very early stage of data cleaning to the very final step of knowledge production and decision support, bridging the gap between raw data mining results and effective decision-making, and resulting in an understandable typology of mental health systems and an actionable domain ontology for mental health systems in low and middle income countries. Including domain experts in the process is part of the methodology itself and is critical to guarantee both the validity and the understandability of results.
Although the process is presented in the particular case of the World Health Organization's mental health data, the methodology can be also applied to other domains that can contribute to improving human life, from policies to improve air quality, to designing healthy lifestyle recommendations, among others.
Karina Gibert (Ph.D.) has been a Full Professor at the Universitat Politècnica de Catalunya-BarcelonaTech (UPC) since November 2018, and and had been a lecturer at UPC since 1990. She is currently member of the board of the Intelligent Data Science and Artificial Intelligence Research center at UPC.
With a Bachelor in Informatics Engineering (computational statistics and AI) and a Ph.D. in Computer Science, she leads several under- and post-graduate courses on Statistics, Multivariate Analysis, Data Mining, Data Science and Intelligent Decision Support, mainly at UPC.
Her experience as a researcher includes health domains, environmental systems, business, e-commerce, tourism and fraud detection. She is currently strongly focused on integral data- and knowledge-driven Data Science processes, and on bridging the gap between data and knowledge extraction processes for complex decision-making.
Essential characteristics of her research are multidisciplinarity, externalization, internationalization and technology transfer. Her scientific production is extensive and highly rated (44 SCI-papers, more than 300 publications, 22 in QI, H-index 24).
She received awards including the donaTIC 2018 Award in the academic/researcher category (Generalitat de Catalunya, Dec. 2018) and the first HackingBullipedia Award (2013), as part of the contest promoted by world-renowned chef Ferran Adrià.
She is also a member of several noteworthy organizations, including the governmental working team Catalonia.AI, a co-writer of the Catalan Strategic Plan for AI, Vicedean of AI of the Official Chamber of Informatics Engineering from Catalonia and a founder of gender commission donesCOEINF and donesIAcat (ACIA) and Women in AI (AEPIA).
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