Checking date: 19/05/2022

Course: 2022/2023

Machine Learning in Healthcare
Study: Bachelor in Data Science and Engineering (350)

Coordinating teacher: MARTÍNEZ OLMOS, PABLO

Department assigned to the subject: Signal and Communications Theory Department

Type: Electives
ECTS Credits: 6.0 ECTS


Skills and learning outcomes
Description of contents: programme
This course aims to provide an overview of the application of machine learning techniques in different problems in healthcare. 1. Introduction to Machine Learning in Healthcare. 2. Smart Electronic Health Records. 3. Disease Identification and Diagnosis. 4. Personalized Medicine 5. Behavioral Characterization and Modification. 5. Drug Discovery. 6. Epidemic Outbreak Prediction.
Learning activities and methodology
AF1: THEORETICAL-PRACTICAL CLASSES. In them the knowledge that students must acquire will be presented. They will receive the class notes and will have basic reference texts to facilitate the follow-up of the classes and the development of the subsequent work. Exercises, practical problems will be solved by the student and workshops and an evaluation test will be carried out to acquire the necessary skills. AF2: Updated to allegation AF3: STUDENT INDIVIDUAL OR GROUP WORK. AF8: WORKSHOPS AND LABORATORIES. AF9: FINAL EXAM. In which the knowledge, skills and abilities acquired throughout the course will be assessed globally. MD1: THEORY CLASS. Lectures in class by the teacher with the support of computer and audiovisual media, in which the main concepts of the subject are developed and materials and bibliography are provided to complement the students' learning. MD2: PRACTICES. Resolution of practical cases, problems, etc. raised by the teacher individually or in a group. MD3: TUTORING. Individualized assistance (individual tutorials) or in groups (collective tutorials) to students by the teacher. MD6: LABORATORY PRACTICES. Applied / experimental teaching to workshops and laboratories under the supervision of a tutor.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment

The course syllabus may change due academic events or other reasons.