Checking date: 26/06/2019


Course: 2019/2020

Personalized Medicine
(18069)
Study: Master in Information Health Engineering (359)
EPI


Coordinating teacher: MUÑOZ BARRUTIA, MARIA ARRATE

Department assigned to the subject: Department of Bioengineering and Aerospace Engineering

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
It is advised to have completed: - Biosignals & Bioimages - Machine Learning - Deep Learning
Competences and skills that will be acquired and learning results.
Personalized medicine identifies elements that predict the individual┬┐s predisposition to disease and their response to treatment. The goal of the course is to familiarize the students with the tools and methods of personalize medicine. Namely, how genomic and -omics information is integrated with clinical and custom laboratory data to classify disease susceptibility, deliver improved diagnoses, earlier interventions, targeted and more efficient drug therapies, and customized treatments. Regarding specific abilities, at the end of the course, the student will be able to: - Demonstrate knowledge on diverse technologies to generate and analyze big data. - Apply Machine Learning tools to the analysis of -omic data - Integrate the information of clinical and -omic data - Insight into the future perspectives on personalized medicine Finally, the student will achieve or enhance general capabilities to: - Analyze problems and propose solutions - Apply knowledge to engineering practice - Decompose complex problems and tasks in a structure collection of simpler ones - Integrates multidisciplinary knowledge - Work autonomously and cooperatively
Description of contents: programme
- Definition, mission and vision for personalized and precision medicine. - Omics technologies: Instrumentation, what data are generated and what information is carried out - Information provided by the medical images and the associated -omic technology (radiomics) - Application of Machine Learning and Deep Learning to -omic data - Integration of -omics information with clinical and custom laboratory data - Lessons learn about personalized medicine in the research setting - How to use personalized medicine in the clinical practice? - Future perspectives in personalized medicine
Learning activities and methodology
Teaching methodology will be mainly based on lectures, seminars and practical sessions. Students are required to read assigned documentation before lectures and seminars. Lectures will be used by the teachers to stress and clarify some difficult or interesting points from the corresponding lesson, previously prepared by the student. Seminars will be mainly dedicated to presentations given by specialists in the subject and to interactive discussion with the students, presentation and evaluation of homework. Grading will be based on continuous evaluation (including short-exams, homework, group essays, practical sessions, and student participation in class and Aula Global). Attendance to lectures, short-exams or submission of possible homework is not compulsory. However, failure to attend any exam or submit the exercises before the deadline will result in a grade of 0 in the corresponding exercise and will influence the final continuous evaluation score. The practical sessions may consist on laboratory work or visits to research or clinical centers. A laboratory report will be required for each of them. The attendance to 80% of practical sessions is mandatory. Failure to hand in the laboratory reports on time or unjustified lack of attendance will result in 0 marking for that practice session.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • A. Roy. The emerging precision, personalized medicine and big data analytics approach in healthcare: Big data analytics in healthcare. .. 2017
  • D. Barh, D. Dhawan, N. K. Ganguly. Omics for personalized medicine. Springer. 2016
Additional Bibliography
  • E. Topol. Deep medicine: How artificial intelligence can make healthcare human again. .. 2019
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.