SE3.1) Estructurar datos a partir de fuentes multimodales y no estructuradas para inferir nuevo conocimiento SE3.2) Evaluar algoritmos de análisis de imagen destinados a solucionar problemas específicos de salud SE3.3) Analizar críticamente la interpretación derivada del análisis de imágen médica SE3.4) Desarrollar herramientas de asistencia al diagnóstico y toma de decisión en salud SE3.5) Integrar datos -ómicos y moleculares a través del análisis funcional de éstos SE3.6) Analizar críticamente la interpretación derivada del análisis de datos moleculares SE3.7) Aplicar técnicas de sistemas complejos a datos epidemiológicos SE3.8) Analizar críticamente la interpretación derivada del análisis de datos de salud pública
Tipus d’activitat Hores amb professor Hores sense professor Hores virtuals amb professor Total Anàlisi / estudi de casos 16,00 0 7,50 23,50 Aprenentatge basat en problemes (PBL) 0 20,00 15,00 35,00 Prova d'avaluació 0 4,00 1,00 5,00 Sessió expositiva 0 1,00 2,00 3,00 Sessió participativa 0 8,00 8,00 16,00 Sessió pràctica 0 20,00 10,00 30,00 Total 16,00 53,00 43,50 112,5
Russell, Stuart J. (Stuart Jonathan) (2021). Artificial intelligence : a modern approach (Fourth edition). Upper Saddle River: Pearson. Catàleg Chen, Li-Pang (2020). Artificial intelligence for drug development, precision medicine, and healthcare. Chapman and Hall/CRC. Eric Totol (2019). Deep medicine : how artificial intelligence can make healthcare human again. Basic Books. WHO (2024). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. WHO. Lei Xing, Maryellen L. Giger, James K. Min (2020). Artificial Intelligence in Medicine Technical Basis and Clinical Applications. Elsevier / Academic Press. Evelyn J.S. Hovenga AM, Heather Grain (2026). Roadmap to Successful Digital Health Ecosystems. A Global Perspective. Elsevier / Academic Press. Simon GJ, Aliferis C (2024). Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls. Springer.
Activitats d'avaluació: Descripció de l'activitat Avaluació de l'activitat % Recuperable Problem-based learning Evaluation criteria will be provided with the problem description. 30 No Test Each question and exercise of the test will be annotated with the correspoing points contributing to the test score. 20 No Online oral tests. Participative exercises during the session. Attending to the presentation sessions. Criteria on each part of the presentation will be provided in the exercise description. 30 No Labs Each practice will be provided with the corresponding score criteria. 20 No
The final mark of the subject will be calculated according to the weights of each of the proposed activities. Declaration of allowed use of AI: This module promotes the critical and responsible use of artificial intelligence tools as part of the learning process. In some activities, the use of AI may be required or permitted, always in accordance with the specific instructions from the teaching staff, which can be found in the virtual learning environment. There may also be activities where their use is explicitly prohibited. In case of doubt regarding their use, students are advised to consult the teaching staff beforehand. Any use of artificial intelligence tools must be duly declared by the student, in accordance with the declaration instructions provided by the teaching staff. Unauthorised use of artificial intelligence tools will be sanctioned in accordance with current regulations. Fraudulent conduct in assessment activities: Fraudulent conduct in any assessment activity, by any means, will result in the student receiving a failing grade for that activity. Furthermore, depending on the severity of the misconduct, the school may propose the initiation of disciplinary proceedings, which shall be formally instituted by a decision of the Master Academic Committee. Criteris específics de la nota «No Presentat»: NP will be considered when the students do not submit any of the evaluation activities. Avaluació única: This is an interuniversity program, that does not consider this kind of evaluation. Requisits mínims per aprovar: The minimum qualification to pass the course is 5.0
To stablish the apointments, students can user or sent mails to the professors. These appointments can be done online via googlemeet / zoom / TAEMS metting.
The communication and interaction with the students will be mainly done via moodle, having also specific forums for the activities. Students can also interact with the professors via email or via videoconferences (googlemeet, zoom, TEAMS).
This subject is conducted in collaboration with Prof. Rui Alvez (from Universidad de Lleida) Updated information in the Moodle of the Master site https://www.urv.cat/en/studies/master/courses/health-data-science/