{"id":9803,"date":"2020-06-10T13:50:10","date_gmt":"2020-06-10T12:50:10","guid":{"rendered":"https:\/\/www.bio-sistemak.eus\/proyecto-departamento-desarrollo-economico-infraestructuras-gobierno-vasco-hazitek-2019-cognitia\/"},"modified":"2021-04-30T13:47:24","modified_gmt":"2021-04-30T12:47:24","slug":"basque-government-project-hazitek-2019-cognitia","status":"publish","type":"page","link":"https:\/\/www.bio-sistemak.eus\/en\/basque-government-project-hazitek-2019-cognitia\/","title":{"rendered":"Hazitek 2019 – COGNITIA"},"content":{"rendered":"

COGNITIA: Deep Learning Systems for the Generation of Actionable Health Knowledge.<\/h2>
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Call for tender for project:<\/strong><\/span> Hazitek 2019\"\"<\/a><\/span><\/p>\n

Funding body:<\/strong> <\/span>Department for Economic Development, Sustainability and Environment\u00a0<\/span><\/p>\n

PI:<\/strong><\/span>\u00a0Eduardo Mill\u00e1n\u00a0<\/span><\/p>\n

Funding awarded: <\/strong><\/span>\u20ac10,000<\/span><\/p>\n

Description: <\/strong><\/span>Health organisations are becoming increasingly interested in the application of Artificial Intelligence (AI) to improve healthcare and to reduce costs and improve the efficiency of healthcare services. The project is based on the results of the OPTIMUS (HAZITEK) and OMEGA3 (ELKARTEK) projects completed in 2018. Based on the needs and future lines of work identified in these projects, this constitutes an industrial research project on Deep Learning technology that offers a qualitative leap in solutions based on AI technology applied to the healthcare sector.<\/span><\/p>\n

Objective:<\/span> <\/strong>Deep Learning technology research, development and assessment to generate actionable health knowledge by means of the integration of predictive models in day-to-day work. As well as this general objective, the project has a series of scientific-technological and socio-economic impact goals.<\/span><\/p>\n

Study design: <\/strong><\/span>The project will approach both non-appropriate interventions that must be reverted and recommendations based on proposed evidence to be incorporated into practice. In order to make the report easier to read, they will be referred to using the generic expression low-value interventions (LVI). The analysis of these interventions is especially important in chronic patients. This project includes a case study focused on Congestive Heart Failure (CHF). The case study will be based on a dynamic cohort, created using data from 2010 onwards from patients aged 40+ admitted to hospital with a first episode of CHF. Baseline data will include patients from 2013-18 (approximately 19,000 people), which is when Presbide (electronic prescription system) was deployed in the Osakidetza information system.<\/span><\/p>\n

Results: <\/strong><\/span><\/p>\n