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Principles of Health Data Science
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[ms_accordion_item title=”Coordination” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]Pedro Pereira Rodrigues 
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Intended learning outcomes of the curricular unit” color=”#7b89b6″ background_color=”” close_icon=”” open_icon=”” status=”open”]
This curricular unit aims to endow the students with the knowledge, skills and behaviors that enable them to identify, interpret and try out modern methods of health data science. Specifically, it is intended that students be able to:
– Identify different research questions relevant to the science of health data.
– Define the science of health data as a process of generation, collection and analysis of data, and its transposition to health decision.
– Describe objectives, procedures, techniques and functional areas of the health data science process.
– Observe real knowledge applications obtained with health data science processes.
– Interpret, and integrate in daily practice, published results with analysis of large health databases.
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[ms_accordion style=”simple” type=”1″ class=”” id=””] [ms_accordion_item title=”Syllabus” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Health data science
• Definition of scientific discipline
• Aggregation of evidence, generation and validation of hypotheses
• Current impact on society
The process of health data science
• Collection and management of health data
• Intelligent health data analysis
• Transposition for health decision
Examples and controversies
• International applications of health data science
• National Contextualization of Health Data Science Applications
• Use of secondary data for alternative purposes
• The power of the new methods of analysis compared with traditional clinical studies
• Impact on the confidence building of all stakeholders (citizens, health professionals, researchers, legislators and decision-makers)
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The content taught will provide students with the necessary and sufficient concepts to understand and initiate modern methods of health data science, and specifically the interpretation of the scientific discipline as an interdisciplinary computer process, intelligent data analysis, and its evaluation and management with application to support clinical decision-making and health services.
Specifically, through the content taught the students will acquire the knowledge and skills that allow them to identify both the objective and the process of health data science, their integration into society, and the controversies that will arise with their generalization
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[ms_accordion_item title=”Teaching methodologies” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Theoretical and theoretical-practical classes with theoretical exposition and discussion of topics, group and individual exercises. Assessment by introductory group work (30%) and final exam (70%).
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The theoretical exposition allows to transmit to the students the concepts that allow them to describe, identify and characterize the aspects related to the theory and practice of health data science. By discussing topics and conducting group work, students will also develop skills and mainly adjust behaviors that allow them to integrate these new methods into the daily practice of research.
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[ms_accordion_item title=”Main bibliography” color=”” background_color=”” close_icon=”” open_icon=”” status=”open”]
Berthold, M., & Hand, D. J. (2003). Intelligent Data Analysis. Springer Berlin Heidelberg.
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