Master in Artificial Intelligence and Big Data in Health


Master (M) | Diploma (D) | Specialization course (C)


M: 20| D: 15 | C: 40






M: 60 ECTS | D: 35 ECTS | C: 10 ECTS


M: €5.880 | D: €3.430 | C: €980


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The training in artificial intelligence in health is presented with 3 training modalities:

  • Master's in Artificial intelligence and Big Data in Health: the most complete training, with 2 years duration, 60 ECTS credits, 5 subjects out of 10 ETCS and Final Master's Thesis.
  • Specialization Diploma: 1 year duration, 35 ECTS credits, first 3 Master's subjects and Diploma Final Project.
  • Specialization course in Artificial intelligence: 10 ECTS credits corresponding to the first subject of the Master's (once this has been completed, it can be extended to any subject of the Master's and with the possibility of adaptation to the Diploma or Master's program when the corresponding subjects have been completed).


Our society is in a profound revolution that will possibly surpass the invention of the steam engine, the train, electricity or mass production in the magnitude of the changes it will bring. This Fourth Industrial Age gravitates around artificial intelligence (AI), robotics and Big Data herald a profound revolution that is already visible in the way we live and work, perhaps even in the way we see ourselves as humans.

This revolution will also affect medicine which is in a certain form at a time of change. As a profession, despite the extraordinary advances in the art and science of medicine in the last four decades, it often presents limitations in diagnosis, especially in its predictive capacity and considering that unnecessary tests and treatments raise costs of public health systems. This revolution can greatly help to deal with these problems.

The potential that the use of large amounts of data gives us is very great, since through the ability to extract knowledge and learn that they have when combined with artificial intelligence and deep learning methods, we can achieve great accuracy in diagnosis and prognosis. With the help of these technologies, clinicians will be able to increase their effectiveness and above all their efficiency in patient care, which is perhaps one of the biggest problems in medicine today.

Therefore, healthcare is one sector that will benefit greatly from AI. AI will save millions of euros to improve the prevention, diagnosis and treatment of problems such as childhood obesity, cardiovascular diseases and their sequelae, neurodegenerative diseases, breast cancer, nephrology, among other areas. In addition, it will allow the development of new medicines and promote personalized and home medicine and improve the quality of life of the elderly.

Personalized medicine has created a new paradigm where few doctors have the proper training. The professionals involved in the healthcare environment need, first of all, the knowledge to deal with a very important change in the way medicine is practiced. Secondly, the specific knowledge that will allow them to address the generation and development of knowledge related to the technologies involved in this new paradigm and, finally, they need the ability to create multidisciplinary teams, which integrate professionals from the scientific and d 'engineering, to address the new challenges posed by this personalized medicine.

In this context, an AI capable of efficiently assisting medical professionals in their decisions and improving methods of person-computer interaction is needed. Currently, physicians rely on clinical guidelines or their experience. Guides may have the limitation of covering only a portion of the clinical practice and experience the biases associated with it. Automatic assistance, capable of performing these probability calculations in a normative manner and with real-time access to electronic medical record data, would allow greater productivity for healthcare professionals. Training for the existence of a new generation of more technological doctors and able to help in the design of these cognitive assistants is one of the challenges in this regard.

The so-called "P4 Medicine" (predictive, personalized, preventive and participatory), will be based on emerging technologies such as AI and the analysis of large amounts of data based on machine learning and computer vision. Thus, data science will be routinely applied to information, structured and unstructured, from electronic health records-omics (genomics, proteomics, transcriptomics, etc.) and medical imaging tools.

Així, this Master, Specialization Diploma i Specialization Courses they are aimed at clinicians interested in learning how AI is applied in health and research with the data they have access to in their workplaces, clinical histories and other data collected by the services where they work. Generally, these professionals are used to research using statistical techniques and now want to go a step further and see what the computational techniques of artificial intelligence can do and how massive data can be treated. In addition, the ethical implications when conducting research are of utmost importance in this area of ​​health, which is why in this Master's the students will acquire the necessary knowledge about privacy, ethics and legality that are needed to write a project proposal that can be validated by the Ethical Committees for Clinical Research with medicines (CEICm).


General objectives

The fundamental objective of these studies is to acquire the necessary knowledge to analyze the information needs that arise in the environment of Health and follow all the stages of the process of building a solution to improve knowledge and decision-making in this area without forgetting the legal and ethical part.

The training objectives of the program are:

  • Train professionals specialized in the processing and analysis of health-related data by using the tools provided by AI and Big Data.
  • Acquire the specific knowledge to analyze the data generated and facilitate decision-making in the environment of Health following all the stages of the process of creating a solution to improve knowledge and decision-making in this area without forgetting the legal and ethical conditions.
  • Develop the capacity to work and create/integrate multidisciplinary teams to address the new challenges posed by this personalized medicine.

Considering the Spanish Qualifications Framework for Higher Education (BRESSOLES) and its deployment as the Catalan Qualifications Framework for Higher Education (2023), learning outcomes are used as the combination of knowledge, skills and competencies that students will be able to demonstrate at the end of the educational process and that for the present studies are:


  • Recognize health data generation technologies.
  • Identify environments based on Artificial intelligence (AI) and the models used.
  • Identify specific technologies and concepts in the field of massive data (Big Data).
  • Demonstrate knowledge in AI and Big Data environments/applications/models in Health.


  • Analyze the transformations and treatment of data in the field of Health Sciences.
  • Relate all aspects linked to data in the field of Health Sciences.
  • Analyze AI methodologies applied to medical data.
  • Use environments and tools for managing massive data.
  • Determine efficient data management environments and structures.
  • Experiment with AI and Big Data tools and models.


  • Evaluate different aspects related to data in the field of Health Sciences.
  • Designing environments for the processing of medical data.
  • Validate the technological tools for mass data management.
  • Design a massive data processing code.
  • Building processing environments based on AI and Big Data.


  • Seats:
    • Master: 20.
    • Specialization diploma: 15.
    • Specialization courses: 40.
  • Credits:
    • Master: 60 ECTS.
    • Diploma of specialization: 35 ECTS.
    • Specialization courses: 10 ECTS.
  • Language: Castellà.
  • mode: virtual.

The program is designed using a virtual methodology through the virtual campus (CV) of the UAB that uses the educational platform *Moodle and which allows learning autonomously and conducive to reflection on the Master's content.

All the contents of the subjects are housed in this virtual campus and from where the different activities that make up the courses are carried out: study of the material, resolution of practical cases or exercises and follow-up and evaluation activities.

At the beginning of each week, the teacher will publish the material to be worked on during the week and will be available through the communication tools of the CV so that the students can formulate the questions and doubts they have. On Fridays, the teacher will conduct a synchronous class to discuss with the students the topics discussed through the *Teams platform which will be recorded and made available to those students who cannot attend (these will be saved until the end of the subject) . The teacher will also include follow-up activities to evaluate the students' progress and the subject will end with a final subject assignment where the student will be able to put into practice all the concepts developed throughout the subject.

Students in each subject will have the subject coordinator who will be able to guide them in their personal progress and resolve their doubts and problems that arise related to this subject or transfer this to the master's coordinators if the problem beyond the particular subject.

Duration and teaching planning

The subjects last approximately 12 weeks. They follow a chronological sequence of the study topics, which incorporate readings, debates and activities linked to the topics studied. This is an approach where the student must overcome the academic requirements (theoretical and practical) of the module in each subject.

Final subject work

The students will have to do a final project for each subject which will consist of planning and carrying out an analysis and development project focused on the topics covered, with the subject coordinator being the student's tutor during this work (or the teacher of the subject to whom he delegates)

Subject evaluation procedures

The five master's/diploma subjects (with the exception of the TFM) will be assessed in a similar way. The interest and degree of involvement demonstrated by the student (intervention in the CV forums), the result obtained in the questionnaires and the weekly follow-up activities and the completion and solution of the final work of the subject will be assessed. The weight of each of these activities will depend on the particular subject and will be indicated at the beginning of each of them, but will be within the following ranges:

  1. Participation: 15-25%
  2. Follow-up activities: 40%
  3. Final work of the subject: 30%-45%

The TFM/TFD will be evaluated as follows:

  1. Report and defense (court): 60%
  2. Tutor follow-up and assessment: 40%

The overall mark of the master's degree and the postgraduate diploma will be the weighted average of the marks of the subjects that make up these programs.

General programs


Edition 2024 - 2026

Subjects 1, 2, 3, 4, 5 + Master's Final Thesis
Subjects 1, 2, 3
Specialization course
Subject 1
Subject 1                                                     From 23/09/24 to 15/01/25
Subject 2                           From 07/01/25 to 20/04/25
Subject 3                           From 21/04/25 to 31/07/25
Subject 4 From 15/09/25 to 15/01/26
Subject 5 From 12/01/26 to 11/04/26
Master's Thesis 30/09/26 (Deadline: 31/01/27)


Data analytics has been used intensively and extensively for many organizations. In the life sciences, clinical data analysis is increasingly popular, one could even say increasingly essential. AI applications can greatly benefit all parties involved in the healthcare sector. For example, data analytics can help healthcare organizations make management decisions, doctors identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The enormous amount of data generated by healthcare transactions is too complex and voluminous to be processed and analyzed by traditional methods. Artificial intelligence applied to data analysis provides the methodology and technology to transform these huge amounts of data into useful information for decision-making.

The Master's degree studies are organized into 5 compulsory subjects of 10 ECTS and a Final Master's Thesis (10 ECTS)

  • S1: Artificial Intelligence in Health.
  • S2: Health Data Analysis.
  • S3: Information processing and management in Big Data environments.
  • S4: Acquisition, filtering and data security.
  • S5: Data storage and display.
  • Master's Final Thesis.

The studies of Specialization Diploma are organized in 3 compulsory subjects of 10 ECTS and a Final Diploma Project (5 ECTS)

  • S1: Artificial Intelligence in Health.
  • S2: Health Data Analysis.
  • S3: Information processing and management in Big Data environments.
  • Diploma Final Project

El Artificial Intelligence in Health specialization course corresponds to the subject module 1 of the Master (10 ECTS)

  • S1: Artificial Intelligence in Health.

It is possible to take the individual subjects as Specialization Courses. You must first take the subject "Artificial Intelligence in Health", to be able to take the rest of the subjects offered. These can be studied either within the Diploma or the Master's, or individually, as well as in any of the editions of the Master's and it is then possible to make an adaptation to have a higher study (e.g. subjects 1,2,3 are possible ,XNUMX as specialization courses and then make the adaptation to enroll in the TFD and obtain the specialization diploma).

Subject 1: Artificial intelligence in health

This subject presents the fundamental concepts for entering the world of AI and data science in health. The aim is to define the context of this area and the necessary concepts, as well as the characteristics involved in a clinical data analysis project and what it means to participate, both in terms of procedure, quality, privacy, security or ethics.

At the end of the course the student will have an overview of what a data analysis project is using AI techniques in health.


  1. Introduction to AI for Clinicians: Applications in Health. Definition and concepts of AI and data science.
  2. Data mining process: Definition of the problem, capture, preprocessing, analysis, visualization and evaluation of the data.
  3. Data learner: Supervised learning concepts vs unsupervised Evaluation of models.
  4. Learning algorithms: Algorithms of Machine Learning i Deep Learning. metrics Use cases
  5. Data storage and visualization: Types of clinical data, structured/unstructured data, relational and non-relational databases (NoSQL). Data visualization.
  6. Massive Data and cloud computing: Concepts linked to Big Data. Processing and analysis. Infrastructures Cloud. Tools and use cases.
  7. Legality in the treatment of medical data and AI: Regulation on AI. Legal foundations for the treatment of clinical data. Main requirements, measures or obligations. Legality in the treatment of medical data and AI. regulations
  8. Data quality, privacy and security: Technical aspects of information privacy and security. ISO/IEC 27001 standard and National Security Scheme (ENS).
  9. Ethics in the treatment of medical data: Concepts on ethical aspects of an AI project: ethically permissible, fair and non-discriminatory, safe, accurate, reliable and transparent.
  10. Innovation and Project Management in Health: Essential elements for creating a funding request proposal for a project related to AI and health. Use cases

Teaching team

  • Ana Benavent
  • Luis Bernaldez Balado
  • Helena Boltà Torrell
  • José Ibeas López
  • Edwar Macias Toro
  • Marcela Manríquez Tapia
  • Antoni Morell Pérez
  • Coloma Moreno Quiroga
  • Guillem Reig Loncán
  • Dolores Rexachs del Rosario
  • Miguel A. Seguí Palmer
  • Javier Serrano
  • Remo Suppi Boldrito

Subject 2: Health data analysis

A key part of data analysis is computational learning techniques. These are the heart of ‘Big Data’ as they allow us to go beyond data and information to infer knowledge. This module 'disconnects' from the size of the data to focus exclusively on the techniques that allow it to be treated in a way that allows it to answer questions that are not obvious when dealing with very varied information of the elements that make up the environment.

The student will be able to select the appropriate techniques for the specific problems that arise, understand their complexity and measure the efficiency of the proposed resolutions.


  1. Statistical tools and data processing: Use of statistical tools to obtain information from the data. Data management and missing values, data compression (PCA and LDA).
  2. Learning algorithms: Evaluation of learning algorithms through
    of the different metrics available.
  3. Linear and logistic regression: Operation and interpretation of the methods
    linear regression and logistic regression for classification problems.
  4. Supervised learning and joint learning models: Regularization concept. L1 and L2 regularization in linear and logistic regression. Evolution in Support Vector Machines (SVM). Joint learning concept (bagging, boosting and model composition). Application to decision trees (RF and GBM).
  5. Unsupervised learning: Concept of unsupervised learning. Clustering and k-Means technique. Association rules.
  6. Fully connected neural networks and for unstructured data: Concept, operation and practical application of RNs. fully connected (MLP). Evolution of the MLP: CNN and RNN.

Teaching team

  • Albert Ruiz Cirera
  • José López Vicario
  • Edward Macias Toro
  • Antoni Morell Pérez
  • Antoni Lozano Bagen

Subject 3: Information processing and management in Big Data environments

The need for massive data processing is a reality that takes advantage of the power of distributed computing infrastructures and the increasing availability of unstructured or semi-structured data.

This module presents a balanced structure between the most important concepts of the subject and practical use cases aimed at carrying out meaningful experiences on real infrastructures.

Thus, the student learns the basics about distributed processing of large volumes of data and receives a practical introduction to some of the technologies and tools currently used in this field.


  1. Introduction to Big Data: Description of the most important concepts related to massive data from its acquisition, filtering, processing, storage and visualization.
  2. Introduction to Linux, Cloud and Python: Basic knowledge for the development and use of tools and programming environments for Big Data processing.
  3. Introduction to code generation with generative AI: Applied knowledge for code development using aids based on generative AI.
  4. Big Data Infrastructure I: Hadoop Environment. Main concepts and use cases of the Hadoop big data processing platform and its ecosystem.
  5. Big Data Infrastructure II: Spark Environment. Key concepts and use cases of the Spark big data processing platform and its ecosystem.

Teaching team

  • Remo Suppi Boldrito

Subject 4: Data acquisition, filtering and security

The data you work with are nothing more than a simplified and sometimes biased representation of reality. The data is captured through physical devices that do not "see" the complete reality but only one or a few aspects of it. The fidelity with which this data represents reality depends on how and with which devices it is acquired.

Also, the data captured must be treated, preserving the information they contain, for their subsequent storage and exploitation. In this subject you will see how the data is captured at its lowest level, and what types of basic treatments are applied to them to get them to reflect reality in the most complete and faithful way on the one hand and to facilitate its storage and /or transmission by another. Whether you work with medical data or, in general, with “sensitive” data, a fundamental part of data processing consists of defining which levels of security and/or privacy are required and how they are guaranteed.


  1. Types of data in health. Description of data types and their meaning: clinical data, vital signs, sequences of events, text, medical images, genome.
  2. Data acquisition: Signals and systems. Transducers. Conditioning, Amplified and Filtered. sampling
    Sensors. Basic concepts Type. Sensor networks. Multimodal information sources. Extraction of characteristics. Medical images: CT, magnetic resonances, ultrasounds, SPECT and PETs. Visualization genomics PCRs.
  3. Data processing: Data reliability. Fault tolerance, availability and cost of storage.
    Compression of text, image and video data. Compression with and without loss, quantification and distortion measurements.
    Compression standards. I SAY. Data pre-processing: cleaning, enrichment, integration and curation.
    Anonymization pseudo-anonymization
    Introduction to security. Security and encryption. Data security and Big Data.

Teaching team

  • Joan Bartrina Rapesta
  • Bernat Gastón Braso
  • Ramon Martí Escalé
  • Joan Oliver Malagelada
  • Marta Prim Sabriá
  • Mercè Villanueva

Subject 5: Storage and visualization of data

This subject is divided into two functional parts: Storage and Visualization of the data. In the first part the student will analyze the type of medical data and its level of structure and will learn to make an appropriate representation for each of them sets of data and will deepen the use of relational databases, for data strongly structured, and non-relational databases, for less structured data.

In the second part, the student will work with key concepts in data visualization, develop visualization structures and create complex visualizations with advanced tools to use data visualization as an exploratory analysis tool.


  1. Introduction to Relational Databases and Data Warehouse: Database Architectures. Client/Server architectures. Types of databases: relational and non-relational. Introduction to database management tools: Oracle, MongoDB, among others. Data warehouse: Introduction to the concept and its management. Options and data exploitation.
  2. Relational databases: The entity-relationship model. Design criteria of an entity-relationship system. Design phases of a database. Capture and analysis of requirements. Relational model. Structure of the data. Integrity rules. Manipulation of the data. Introduction to SQL.
  3. Non-Relational Databases: Introduction to the concepts of NoSQL databases, architectures, types, differences and data exploitation.
  4. Integration and homogenization of data: Introduction to these concepts and their implementation.
  5. Data Visualization: Key concepts in data visualization, visualization structures. Advanced tools for using data visualization as an exploratory analysis tool.

Teaching team

  • Helena Boltà Torrel
  • Oriol Ramos Terrades
  • Aura Hernández Sabaté
  • Laura Rivera Sanchez
  • Carlos Sanchez Ramos

Master's Thesis (TFM)

This is a deepening and development of the concepts worked on in the subjects and its main objective is to evaluate the integration of the knowledge, skills and competences acquired during the teaching of the subjects.

The student must develop a study or project based on open data or available data that have passed the relevant legal requirements to which the student will apply methods, tools and procedures already worked on during the development of the subjects.

Follow-up tutorials

The student will have a dedicated tutor who will guide and supervise the work throughout its execution.

Final report and presentation

The student must present a final report, in journal article format, with a length of between 10-15 pages on: Summary, Introduction and State of the Art, Material and Methods, Discussion, Results, Conclusions and Open Lines , Bibliography and Acknowledgments. Additional pages (appendices) may be added to include additional material, graphics, figures that are complementary to the work developed. The format, preferably, will be IEEE and will have to be delivered through the Virtual Campus.

The evaluation will be carried out through a panel made up of three professors in the public presentation. Before the exhibition, the student must present a video (between 5 and 7 minutes) explaining the work done.

Teaching team

  • Bernat Gastón Braso
  • Aura Hernández Sabaté
  • Jose Ibeas Lopez
  • José López Vicario
  • Antoni Morell Pérez
  • Oriol Ramos Terrades
  • Dolores Rexachs del Rosario
  • Laura Rivera Sanchez
  • Carlos Sanchez Ramos
  • Javier Serrano Garcia
  • Remo Suppi Boldrito

Teaching team

Joan Bartrina: doctor Professor in the Department of Information and Communications Engineering at the UAB. Data compression area, video and digital satellite and medical images.

Anna Benavent: MSc in Telecommunications and Electronics Engineering Sciences. Director of Organization and Information Systems. Park Taulí University Hospital.

Luis Bernaldez: Engineer. Head of systems and communications at the Parc Taulí University Hospital.

Helena Boltà: MSc in Visual Analytics and Big Data. Professor in the Department of Computer Science and Artificial Intelligence. UAB Big Data area and databases.

Bernat Gaston: doctor Professor in the Department of Information and Communications Engineering at the UAB.

Aura Hernandez: doctor Professor in the Department of Computer Science and Artificial Intelligence. UAB

Jose Ibeas: doctor nephrology Park Taulí University Hospital. Director of the Clinical, Interventional and Computational Nephrology Group (CICN) of the Parc Taulí Research and Innovation Institute (I3PT). MSc in Evidence-Based Medicine. President of the Spanish Multidisciplinary Vascular Access Group (GEMAV). Member of the Medicines Research Ethics Committee (CEIm) at the Parc Taulí University Hospital

José López-Vicario: doctor Professor in the Department of Telecommunications and Systems Engineering. UAB
Antoni Lozano: Doctor. Professor of the Department of Mathematics. UAB

Edward Macias: doctor Engineer at Cognizant Netcentric.

Marcela Manríquez: Degree in Medicine. Coordinator of the Clinical Trials Unit. Member of the Research Ethics Committee. Park Taulí University Hospital.

Ramon Martí: doctor Professor in the Department of Information and Communications Engineering. UAB Area of ​​Computer Networks, Security and Computer Architecture.

Anthony Morell: doctor Professor in the Department of Telecommunications and Systems Engineering. Medical Data Analytics Area.

Coloma Brown: MSc in Public Health. Doctor specialist in Preventive Medicine and Public Health., Specialty in Epidemiology. Technical Secretariat of the Research Ethics Committee. Park Taulí University Hospital.

Marta Prim: doctor Professor in the Department of Microelectronics and Electronic Systems. UAB

John Oliver: doctor Professor of the Department of Microelectronics and Electronic Systems. UAB Integrated Circuits Design Area.

Oriol Ramos: doctor Professor in the Department of Computer Science and Artificial Intelligence. UAB

William King: lawyer MSc in international business law. Specialist in health law and personal data protection. Member of the Research Ethics Committee of the Parc Taulí University Hospital

Dolores Rexachs: doctor Professor in the Department of Computer Architecture and Operating Systems. Area of ​​computer architecture and intelligent systems oriented to health services.

Laura Rivera: doctor Professor in the Department of Computer Science and Artificial Intelligence. UAB

Albert Ruiz: doctor Professor of the mathematics department. UAB Area algebra, geometry and topology.

Carlos Sanchez: doctor Professor in the Department of Computer Science and Artificial Intelligence. UAB

Miguel Angel Followed: doctor Specialist in Medical Oncology. Head of the Oncology Service and President of the Research Ethics Committee of the Parc Taulí University Hospital.

Javier Serrano: doctor Professor (on leave) in the Department of Telecommunications and Systems Engineering. UAB Researcher at the Technology Innovation Institute. Abu Dhabi. Arab Emirates

Rem Suppi: doctor Professor in the Department of Computer Architecture and Operating Systems. Area of ​​big data and AI, distributed systems and infrastructures for data processing (clusters and Cloud).

Mercè Villanueva: doctor Professor in the Department of Information and Communications Engineering. UAB Area of ​​optimization of coding in digital transmissions and data storage applied to the information society.


The course is aimed at professionals in the field of Health Sciences: Medicine, Pharmacy, Nursing and other graduates related to Health Sciences. For other degrees, the profile and curriculum vitae of the student will be assessed.

There will be a 10% discount for members of the Parc Taulí University Hospital, Bioinformatics Barcelona and 5% for UAB Alumni.

Steps to follow to formalize the registration:

1. Send your details to the Technical Secretariat

    First and last name



    Postal code




    Work placement

    Modality of training

    2. Send the documentation

    The documentation that must be submitted is:

    Professionals with Spanish nationality:

    • University degree, with original or digital certificate (Spanish nationality)
    • Photocopy of DNI/NIE/Passport

    Professionals with the nationality of countries that are signatories to the Hague Convention (Postilius Convention):

    • University degree and the academic file with the postil of The Hague
    • Photocopy of DNI/NIE/Passport

    This documentation must be mailed to:

    Ester Freixa
    Fundació Parc Taulí
    Parc del Taulí, 1, Santa Fe Building, 2nd left floor
    08208 - Sabadell (Barcelona)

    3. Make the payment

    • Master: 5.880 €
    • Postgraduate Diploma: 3.430€
    • Course: 980€

    Cancellation fees: €50 Course, €142,50 Diploma and €285 Master.

    Discounts: 10% to members of the Parc Taulí University Hospital, Bioinformatics Barcelona and 5% UAB Alumni.

    The amount of the training must be paid by transfer, indicating your name, to the account number:

    ES69 0081 5154 22 0002103622

    Once you've made your payment, you'll need to email ( the receipt of the transfer. At this time, your place will be reserved automatically.

    In case the registration payment is made through a company, you will need to send an email to to provide tax information:

    • Company name
    • Address, postal code and population
    • Tax ID No.
    • Contact person and email

    venue and contact

    Master's degree venue

    Parc Taulí Hospital Universitari. Universitat Autònoma de Barcelona
    Parc del Taulí, 1
    08208 Sabadell (Barcelona)

    The academic activity will be carried out through the UAB Virtual Campus platform (Moodle, and synchronous sessions through the Teams platform.



    • Jose Ibeas. Nephrology Service. Parc Taulí University Hospital.
    • Remo Suppi. Department of Computer Architecture and Operating Systems. School of Engineering - Autonomous University of Barcelona.

    Endorsed by

    • Spanish Society of Nephrology
    • Catalan Society of Nephrological Nursing
    • Spanish Society of Dialysis and Transplantation
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