Checking date: 17/05/2023

Course: 2023/2024

Reseach Skills
Master in Machine Learning for Health (Plan: 480 - Estudio: 359)

Coordinating teacher: DESCO MENENDEZ, MANUEL

Department assigned to the subject: Bioengineering Department

Type: Compulsory
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
The subject "Research Skills" aims to provide students with the necessary skills to be able to design and develop research projects in the biomedical field, as well as to adequately report their results. To this end, the student is trained in a series of skills and techniques, of an eminently practical nature, which are very necessary for field work in research, especially in the biomedical sector. This sector is characterized by a strong legal regulation of many aspects, especially those related to work with patients (clinical trials) or laboratory animals, and it is necessary to know this framework to be able to design and execute research projects in this field. On the other hand, biomedical experimentation is characterized by a high biological variability in the results, which forces the analysis of the results to be carried out by means of biostatistical procedures, knowledge of which is absolutely necessary to be able to publish and validate results. Another important aspect covered in the course is how to manage the innovative aspects of research work, in terms of intellectual property protection, possible entrepreneurship, etc. Finally, at a very practical level, it is explained how to write research projects and how to structure and write scientific articles, within the high standards currently demanded by the scientific community.
Description of contents: programme
- Applied Biostatistics - Experimental design and epidemiology - Application of biostatistics concepts to artificial intelligence techniques. - Ethics in research. Good practices. - Design of research projects - Innovation, intellectual property and entrepreneurship. - Introduction to clinical trials - Introduction to biomedical research with laboratory animals. - Writing scientific articles and responding to reviewers. - Workshop on academic work without plagiarism, use of artificial intelligence chatbots, etc.
Learning activities and methodology
AF3 Theoretical practical classes AF5 Tutorship AF6 Team work AF7 Student individual work AF8 Partial and final exams Activity code total #hours presence #hours % Student Presence AF3 19,5 19,5 100% AF5 1 1 100% AF6 16 0 0% AF7 36.5 0 0% AF8 2 2 100% TOTAL SUBJECT 75 22.5 30%
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment
Basic Bibliography
  • Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, Munafò MR. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013 May;14(5):365-76. 2013
  • England JR, Cheng PM. Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. AJR Am J Roentgenol. 2019 Mar;212(3):513-519. 2019
  • Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nat Med. 2019 Jan;25(1):24-29. 2019
  • Ioannidis JP. Why most published research findings are false. . PLoS Med. 2005 Aug;2(8):e124. 2005
  • Mills JL. . Data torturing. N Engl J Med. 1993 Oct 14;329(16):1196-9. 1993
  • Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, du Sert NP, Simonsohn U, Wagenmakers EJ, Ware JJ, Ioannidis JPA. A manifesto for reproducible science. Nat Hum Behav. 2017 Jan 10;1:0021. 2017
  • Seong Ho Park, Young-Hak Kim2 Jun Young Lee3 Soyoung Yoo, Chong Jai Kim. Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review. Sci Ed 2019; 6(2): 91-98.. 2019

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