Disease understanding: Dealing with complex and unstructured big data in biomedical domain

Outline

Big data applications in the Healthcare Sector indicate a high potential for improving the overall efficiency and quality of care delivery.

Unstructured data represents a powerful untapped resource—one that has the potential to provide deeper insights into data and ultimately help drive competitive advantage. This unstructured data now makes up a very significant portion of the data, and all kind of companies care rapidly exploring technologies for analysing this kind of data to gain competitive advantage. Solutions to analyse these kinds of data can be applied in other domains using similar nature data sources.

In the healthcare sector, big data analytics has still to address several technical requirements such as: i) use of Electronic Health Records (EHR) and its implications; ii) preprocessing of natural text iii) annotation of images; iv) dealing with data silos and building of solutions avoiding them, etc.

This seminar focus on the concept of disease understanding, a very relevant field that allows having a better comprehension about diseases, how they are related, and how these relationships can be used for the improvement of the biomedical domain and sector. Disease understanding can be improved through the acquisition and analysis of data from both structured and structured sources. This seminar focus on the retrieval of such information for the aforementioned disease understanding goal.

Syllabus

1. Big Data challenges and problems to be addressed in the medical domain
2. Dealing with Big Data from a medical imaging analysis perspective
3. Human disease networks: large-scale creation and analysis

Assessment Method

Attendance and participation. Short assignment

Remarks

Timetable

• 11 February, 15:00-18:00

Lecture Theatre

• A-5001

Tuition Language

English.