Master in Artificial Intelligence and Big Data in Health

Type

Master (M) | Postgraduate (P) | Specialization course (C)

Seats

M: 20 | P: 15 | C: 40

Size

Online

Language

Spanish

Accreditation

M: 60 ECTS | P: 30 ECTS | C: 10 ECTS

Preus

M: € 5.700 | P: € 2.850 | C: € 950

Open registrations

C: 09/04/21 | M+P+C: 27/09/21

Programs

HTML and PDF

  • Presentation
  • Targets
  • Methodology
  • Programs
  • Teaching team
  • Registration
  • venue and contact

Presentation

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

  • Master in Artificial Intelligence and Big Data in Health: the most complete training, with 2 years of duration, 60 ECTS credits, 5 training modules and TFM.
  • Postgraduate Diploma: 1 year duration, 30 ECTS credits, 3 first training modules of the Master.
  • Artificial Intelligence specialization course: 10 ECTS credits corresponding to the first module of the Master.

Justification

We are experiencing such a profound revolution that it will possibly surpass the invention of the steam engine, train, electricity or mass production in the magnitude of the changes it will bring. This Fourth Industrial Age revolves around artificial intelligence (AI), robotics and big data, advocating a profound revolution that is already visible in the way we live and work, perhaps even in the way we see ourselves. themselves as humans.

This revolution will also affect medicine. Medicine itself is in some form in a time of crisis. As a profession, despite the extraordinary advances in the art and science of medicine in the last four decades, it frequently presents limitations in diagnosis and especially in its predictive capacity; it does unnecessary testing and treatment that raise the costs of medicine. This revolution can go a long way in dealing with these issues.

The fourth industrial revolution

The potential provided by the use of large amounts of data is fantastic. Through the ability to extract knowledge and learn that these data have when we combine them with methods of artificial intelligence and deep learning, we can achieve great accuracy in diagnosis and prognosis. With the help of these technologies clinicians could increase their effectiveness and especially their efficiency in patient care which is perhaps one of the big problems of today’s medicine.

Therefore, healthcare is a sector that would benefit enormously from AI. AI will save billions of euros by improving the prevention, diagnosis and treatment of problems such as childhood obesity, cardiovascular disease and its sequelae, neurodegenerative diseases and breast cancer, among other areas . In addition, it will allow the development of new medicines and the promotion of personalized and home medicine or the improvement of the quality of life of the elderly.

Personalized medicine has created a new paradigm where few doctors have the proper training. Then, the professionals involved in the healthcare environment first need to know him to deal with a very important change in the way medicine is done. Second, the specific knowledge that will allow them to address the generation and development of knowledge related to the technologies involved in this new paradigm. Lastly, they need the ability to create multidisciplinary teams that integrate professionals from the scientific and engineering environments that allow them 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 "Medicine P4" (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.

So this one Master's degree, Postgraduate Diploma i Specialization Courses they are aimed at clinicians interested in learning how AI is applied in health and doing research with the data they have access to their jobs, clinical histories and other data they collect from the services where they work. Generally these professionals are used to doing research using statistical techniques and now they want to go a step further and see what can be given to the computational techniques of artificial intelligence 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 degree students will acquire the necessary knowledge about privacy, ethics and legality needed to write. a project proposal that can be validated by the Clinical Research Ethics Committees (CEIC).

Targets

General objectives

The main objective 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 based on artificial intelligence to improve knowledge and decision making in the area of ​​health without forgetting the legal and ethical part.

The objectives and competencies below correspond to the Master. Those of the Postgraduate Diploma are a subset of these and are indicated with a P.

Specific objectives

  • To train professionals specialized in the processing and analysis of clinical data related to health by using the tools provided by AI and DB. (P)
  • Acquire specific knowledge to analyze the information needs that arise in the Health environment and follow all stages of the process of building a solution to improve knowledge and decision-making in the area of ​​health without forgetting the legal and ethical part. (P)
  • Develop the ability to create multidisciplinary teams that address the new challenges posed by this personalized medicine. (P)
  • Acquire advanced knowledge in the areas of data acquisition, data storage and visualization, security and privacy.
  • Develop a comprehensive clinical data analysis project.

Specific skills

At the end of the Master / Diploma, the student must be able to:

  • Understand health data generation technologies and analyze the needs for pretreatment, storage and data processing that arise in the field of Health Sciences. (P)
  • Design and evaluate a system, process, component or program to meet the needs raised in Health. (P)
  • Apply mathematical methods, algorithmic principles and artificial intelligence to model, design and develop applications, services, intelligent systems and knowledge-based systems in the field of Health. (P)
  • Manage and exploit different types of information related to the field of health to transform it into knowledge. (P)
  • Design, implement and manage systems for the management of massive data sets. (P)
  • Critically analyze security and privacy aspects in a project with clinical data.
  • Develop a clinical data analysis project following all stages, and covering both technical and ethical aspects.

Transversal competences

The student must be able to:

  • Understand the professional, ethical, legal, security, and social aspects, problems, and responsibilities. (P)
  • Work effectively in multidisciplinary teams to achieve a common goal. (P)
  • Communicate effectively with a variety of audiences. (P)
  • Analyze the local and global impact of applying Artificial Intelligence and Big Data Strategies on patients, organizations and society. (P)

Methodology

  • Size: Part-time. Mainly online, with 6 face-to-face classes for each module (see program). Given the current exceptional situation, if necessary all content would be converted online. Also, if any student has problems being able to attend, the content will be recorded in person.
  • Seats:
    • Master: 20.
    • Postgraduate Diploma: 15.
    • Specialization courses: 40.
  • Credits:
    • Master: 60 ECTS.
    • Postgraduate Diploma: 30 ECTS.
    • Specialization courses: 10 ECTS.
  • Language: Castellà.

The program is designed using an interactive methodology through the UAB virtual campus that uses the Moodle educational platform and allows learning in an autonomous way and encourages reflection on the most relevant concepts of the Master.

All the contents of the Master are hosted on the virtual campus of the Autonomous University of Barcelona (UAB). From this virtual campus the different activities that make up the courses are carried out: study of the theoretical material, resolution of practical cases or exercises and realization of self-evaluations.

During the development of the modules, the student will find on the virtual campus various communication tools, such as e-mail, forums and chats. Forums, in particular, allow the exchange of ideas between students and teachers and are a fundamental element of our course. They raise doubts, propose debates and interact with other classmates, always under the supervision of a professor specializing in the subject.

Students will always have the help of a specialized tutor who will guide them in their personal progress and resolve their doubts about the operation of the campus.

Duration and teaching planning

The modules of this Master will be offered sequentially over two years, one module each term (in the summer term nothing is offered).

The modules will last 12 weeks. They follow a chronological sequence of the study topics indicated above, which incorporate readings, debates and activities related to the topics worked on. It is an approach where the student must overcome in each subject the academic requirements (theoretical and practical) of the module.

For each module, 3 face-to-face sessions of 8 hours (Friday afternoon and Saturday morning) will be organized as follows:

  • In the first week of the module, there will be a presentation of the module, a conference given by a relevant speaker and related to the content of the module, lectures and introduction to the practices.
  • Week 6 will be the second face-to-face meeting of the module. It will begin with a lecture given by a relevant speaker and related to the content of the module, master classes, presentations of the work done by the students, internship session
  • The last week of the module (week 12). It will begin with a lecture given by a relevant speaker and related to the content of the module, internship session, presentations of the work done by the students, evaluation session.

Final work

Students will be required to complete final work that will consist of planning and developing a clinical data analysis project.

A tutor will be assigned to advise and supervise the work done by the students.

A final report must be submitted in article format, it will have between 10 and 15 pages of explanation of the work, state of the art, methods, discussion, results and bibliography. Additional pages can be added to include appendix material. The presentation of a video explaining the work done will be valued.

The final work must be presented (oral presentation) before a panel of three members, at least two of whom must be from the teaching staff.

The final evaluation will be made by the evaluation committee, which will participate in the public presentation.

Evaluation procedures

The five modules of the master's degree (with the exception of the TFM) will be assessed in a similar way. The interest and degree of involvement shown by the student (intervention in the CV forums), the realization and defense of the course work, and the result obtained in the questionnaires and activities that will have to be delivered weekly will be valued. The weight of each of these activities in the final grade of each module is shown below:

  1. Intervention in the forums: 25%
  2. Questionnaires: 10%
  3. Mandatory weekly activities: 40%
  4. Course work (face-to-face presentation): 25%

The TFM will be evaluated by a court that will take into account the project report and the public presentation that is made.

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

General programs

Calendars

Edition 2020 - 2022

Master
Modules 1, 2, 3, 4, 5 + Master's Thesis
Postgraduate
Modules 1, 2, 3
Specialization course
Module 1
Module 1

From 25/09/20 to 19/12/20

Module 2 From 08/01/21 to 28/03/21
Module 3 From 09/04/21 to 27/06/21
Module 4 From 24/09/21 to 19/12/21
Module 5 From 14/01/22 to 14/04/22
Master's Thesis Deadline: 30/09/22

Edition 2021 - 2023 (open registrations)

Master
Modules 1, 2, 3, 4, 5 + Master's Thesis
Postgraduate
Modules 1, 2, 3
Specialization course
Module 1
Module 1

From 27/09/21 to 19/12/21

Module 2 From 10/01/22 to 03/04/22
Module 3 From 11/04/22 to 03/07/22
Module 4 From 26/09/22 to 18/12/22
Module 5 From 09/01/23 to 02/04/23
Master's Thesis Deadline: 31/07/23

Contents

Data analysis has been used intensively and extensively by many organizations. In life sciences, the analysis of clinical data is becoming increasingly popular, it could even be said to be increasingly essential. AI applications can greatly benefit all parties involved in the healthcare sector. For example, data analysis can help healthcare organizations make management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable care services. The huge 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 in 5 compulsory modules of 10 ECTS and a Final Master Project (10 ECTS):

  • M1: Artificial Intelligence in Health.
  • M2: Health Data Analysis.
  • M3: Big data environments for data analysis.
  • M4: Data acquisition, filtering and security.
  • M5: Data storage and display.
  • Master's Degree Project.

The Postgraduate Diploma studies are organized in 3 compulsory modules of 10 ECTS each:

  • M1: Artificial Intelligence in Health.
  • M2: Health Data Analysis.
  • M3: Big data environments for data analysis.

El Artificial Intelligence in Health specialization course corresponds to module 1 of the Master of 10 ECTS. :

  • M1: Artificial Intelligence in Health.

There is the possibility of studying individual modules o Specialization courses. The “Artificial Intelligence in Health” module must be taken first, in order to take the other modules offered. These can be taken either within the Postgraduate or Master's, or individually, as well as in any of the editions of the Master.

Module 1: Artificial intelligence in health

This course introduces the key concepts for getting into the world of AI and health data science. 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 and quality, privacy, security or ethics.

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

Content

  1. Introduction to Artificial Intelligence for Clinicians: Health Applications. Definition and concepts of AI and data science: Intelligent agents, decision making, machine learning.
  2. Data mining process data: problem definition, capture, preprocessing, analysis, visualization and evaluation, pre-processing of data (cleaning, integration, reduction, transformation). Experimental design.
  3. Data acquisition and storage and data visualization: Clinical data types, structured vs. unstructured data, relational and non-relational databases (NoSQL). Data visualization.
  4. Learning from the data: Supervised vs. unsupervised learning, Model assessment, Regression, Classification, Segmentation.
  5. Learning algorithms: Decision trees, ensemble methods, deep learning. Typical applications.
  6. Big Data Processing: Features (4 V's), mass data infrastructures, cloud platforms, storage and process models for mass data.
  7. Data quality, privacy and security.
  8. Ethics in the Processing of Medical Data.
  9. Legality in the Processing of medical data.
  10. Health project management.

Teaching team

  • Ana Benavent
  • Luis Bernaldez
  • Helena Boltà
  • José Ibeas
  • Edwar Macias
  • Marcela Manríquez
  • Antoni Morell
  • Coloma Moreno
  • Guillem Reig
  • Dolores Rexachs
  • Ana Ripoll
  • Miguel A. Seguí
  • Javier Serrano
  • Remo Suppi
  • Elena Valderrama

Module 2: Data analysis in health

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.

Content

  • Data analysis health environments: applications.
  • Clinical data and their management and pre-processing.
  • Review of statistical tools
  • Attribute selection (PCA, LDA, ICA)
  • Supervised learning: Assessment
  • Supervised learning: Algorithms
  • Learning with neural networks (MLP, CNN, LSTM)
  • Unsupervised learning: clustering
  • Course project

Teaching team

  • Ramon Baldric
  • Debora Gil
  • Edward Macías
  • Antoni Morell
  • Albert Ruiz
  • Javier Serrano

Module 3: Big Data Environments for Data Analysis

The need for mass data processing is a reality that takes advantage of the power of distributed computing infrastructures and the growing availability of unstructured or semi-structured data. This union allows for analytical capability using the appropriate algorithms to draw conclusions from large volumes of data over reasonable periods of time.

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.

Content

  1. Big Data: impact of data on today's society.
  2. Features of Big Data, Acquisition and Storage.
  3. Environments for distributed BD processing: technology and infrastructure (from cluster to cloud).
  4. Processing, storage and visualization: tools and work environments.
  5. Use cases: Cloudera, HortonWorks, MapR, AWS, Google, Azure.
  6. Data storage models: hierarchical, relational, objects, declarative, graphs, unstructured.
  7. Data processing models: Map-Reduce, Spark, Tuples, Graph, Batch,…
  8. Data visualization models
  9. Case study: integrated data processing and visualization environment (OpenML)
  10. Use cases on medical data: Text-Analytics, Data Processing & Visualization, Search & Classify.

Teaching team

  • Remo Suppi
  • Eduardo César Galobardes

Module 4: Data acquisition, filtering and security

The data we work with is nothing but a simplified and sometimes biased representation of reality. Data is captured through physical devices that do not "see" the whole reality but only one or a few aspects of it. The fidelity with which this data represents reality depends on how and with what devices it is acquired. Likewise, the captured data must be processed, preserving the information they contain, for later storage and exploitation.

In this module the student will learn how to capture data at its lowest level, and what types of basic treatments are applied to (1) get them to reflect reality in the most complete and accurate way and (2) facilitate its storage and / or transmission. Since we will work with medical data or, in general, with “sensitive” data, a fundamental part of data processing is to define what levels of security and / or privacy are required and how they are guaranteed.

Content

  1. Types of health data: clinical data, vital signs, event sequences, text, medical images, and genome.
  2. Data acquisition I: signals and systems, sampling, transducers and sensors.
  3. Data acquisition II: multimodal information sources.
  4. Data reliability. Fault tolerance, availability and storage cost.
  5. Data pre-processing: cleaning, enrichment, integration and curation.
  6. Anonymization. Randomization and generation techniques. Pseudoanonimization.
  7. Compression of text, image and video data. Compression with and without loss, quantification and distortion measurements.
  8. Compression standards. DICOM.
  9. Introduction to security. Security and encryption basics.
  10. Data security. Technologies and security risks in Big Data.

Teaching team

  • Joan Bartrina
  • Bernat Gastón
  • Ramon Martí
  • Joan Oliver
  • Elena Valderrama
  • Mercè Villanueva

Module 5: Data storage and visualization

Part I: Database Integration

In addition to the data captured by medical devices, we will find other types of data that may come from clinical records, databases of other institutions and, in general, from any source that may generate data of clinical interest. The level of structure of this data will depend on the nature of the same. Therefore an adequate representation will be required for each of these data sets.

In this first part of the module the student will learn to represent the data using relational databases, for strongly structured data, and non-relational databases, for less structured data. The last part of the module will be dedicated (through practical examples) how to integrate the different types of databases. 

Content

  1. Databases by data warehouse, basic concepts
  2. Basic SQL queries
  3. Advanced SQL queries
  4. NoSQL databases, what they are, why they are used
  5. Design of a database
  6. Data integration and homogenization, data organization

Part II: Data visualization

Data visualization is becoming a very effective tool for the manipulation, analysis and interpretation of large volumes of data, as it takes advantage of the skills of the human visual system, able to quickly detect patterns, repetitions, discordant elements , etc. A good display is the most effective mechanism to capture the attention of users, giving value to the saying that "a picture is worth a thousand words."

Content

  1. Work with the data, preprocessed and processed to manage the data to be displayed.
  2. Visualizations: types, characteristics, graphic attributes.
  3. Examples of visualizations with different perspectives.
  4. Story telling.

Teaching team

  • Helena Boltà
  • Oriol Ramos

Master's Thesis (TFM)

It will consist of the design and implementation of a project, to answer the questions posed by health professionals, using data analysis technologies in anonymized databases.

Follow-up tutorials

Where the proposals made by students for data analysis will be analyzed and the results obtained will be monitored.

Final report and presentation

The final report will be made in article format, will have between 8 and 10 pages of explanation of the work, thanks and bibliography. Additional pages can be added to include appendix material.

The format will preferably be IEEE, and should include at least an introduction, objectives, state of the art, material and methods, results and discussion, conclusions and open lines, and bibliography.

The final evaluation will be done by an evaluation committee, which will participate in the public presentation. The presentation of a video explaining the work done will be valued

Teaching team

  • José Ibeas
  • Dolores Rexachs
  • Javier Serrano

Teaching team

Ramon Baldrich. Doctor. Computer Engineer. UAB - Computer Science and Artificial Intelligence. Computer Vision Center (CVC)

Joan Bartrina. PhD in Computer Science. UAB - Information and Communications Engineering. Area for data compression, video and digital satellite and medical images.

Anna Benavent. PhD in Telecommunications Engineering. Director of Organization and Information Systems. Parc Taulí University Hospital

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

Helena Boltà Torrell. Master in Visual Analytics and Big Data. UAB - Dept. Computer Science and Artificial Intelligence. Big Data area and databases.

Eduardo Cesar Galobardes. Doctor of Computer Science. . UAB - Dept. of Computer Architecture and Operating Systems

Bernat Gaston. Doctor of Computer Science. I2CAT Foundation

Debora Gil. PhD in Mathematics. UAB - Computer Science and Artificial Intelligence. Group leaderInteractive and Augmented Modeling Research (AMI) al Computer Vision Center (CVC). Researcher in the modeling of heterogeneous data in clinical support systems (diagnosis and intervention) to decisions.

José Ibeas. Nephrology Specialist. Doctorate in Medicine. Master in Evidence-Based Medicine. Postgraduate in Big Data and Artificial Intelligence for Life Sciences. Responsible for the I9PT Group A4G3 - Clinical, Interventional and Computational Nephrology (CICN). Member of the Research Ethics Committee of Parc Taulí University Hospital. President of the Vascular Access Society (VAS) and Vice President of Spanish Multidisciplinary Vascular Access Group (GEMAV).

Edwar Macias. Electronic Engineer. Master in Telecommunication Engineering. UAB - Dept. Telecommunication and Systems Engineering. Wireless Information Networking (WIN). Medical Data Analysis Area. Researcher in the area of ​​model prediction control, signal processing algorithms, speech technologies and medical data analytics, machine learning, data mining, e-health and IoT.

Marcela Manriquez. Degree in Medicine. Coordinator of the Clinical Trials Unit. Member of the Research Ethics Committee. Parc Taulí University Hospital.

Ramon Martí. Doctor of Telecommunications Engineering. UAB - Information and Communications Engineering. Computer Networks, Security and Computer Architecture Area. Security of Networks and Distributed Applications (SeNDA) Research Group. Areas of research in multimedia documents, e-commerce, distributed applications, security and mobile agents.

Antoni MorellDoctor of Engineering. UAB - Dept. Telecommunication and Systems Engineering. Wireless Information Networking (WIN). Medical Data Analysis Area. Researcher in the area of ​​model prediction control, signal processing algorithms, speech technologies and medical data analytics. Experience in optimization techniques applied to communications and IoT / WSN.

Coloma Moreno. Specialist in Preventive Medicine and Public Health. Master in Public Health, specializing in Epidemiology. Technical Secretary of the Research Ethics Committee. Parc Taulí University Hospital.

Joan Oliver. Doctor of Computer Science. UAB - Microelectronics and Electronic Systems. Researcher at the Integrated Circuits Design Group of the Barcelona Institute of Microelectronics. Co-founder of Alternative Energy Innovations SL (AEinnova)

Oriol Ramos. Doctor of Computer Science. UAB - Computer Science and Artificial Intelligence

Guillem Reig. Graduate in law and master's degree 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 Isabel Rexachs del RosarioPhD in Computer Science. UAB - Dept. of Computer Architecture and Operating Systems. Researcher at HPC4EAS group, in the area of ​​Computer Architecture and Intelligent Systems Oriented to health services

Anna Ripoll Araceli. Degree in Physics. PhD in Computer Science. UAB - Dept. of Computer Architecture and Operating Systems. Professor of Architecture and Computer Technology. President of Bioinformatics Barcelona (BIB)

Albert Ruiz. Doctor of Mathematics. UAB - Dept. Maths. Researcher member of Barcelona Algebraic Topology Group (MTM2016-80439-P) i of Lab Interactions between Algebra, Geometry and Topology.

Miguel Ángel Seguí. Specialist in Medical Oncology. Doctorate in Medicine. Head of the Oncology Service and President of the Ethics Research Committee of the Parc Taulí University Hospital. Associate Professor at the Autonomous University of Barcelona. Member of the Taulí of Directors of Spanish Breast Cancer Research Group (GEICAM) y Spanish Society of Medical Oncology (SEOM).

Javier Serrano. Doctor of Computer Science. UAB - Dept. Telecommunication and Systems Engineering. Wireless Information Networking (WIN). Medical Data Analysis Area. Researcher in the area of ​​model prediction control, signal processing algorithms, speech technologies and medical data analytics.

Remo SuppiDoctor of Computer Science. UAB - Dept. of Computer Architecture and Operating Systems. Area of ​​computer networks, distributed systems and infrastructures for data processing (clusters and Cloud). Researcher member of the group HPC4EAS in the field of high-performance simulation based on ABM (Agent Based Modeling) applied to emergency evacuations and disease spread.

Elena ValderramaPhD in Physics. UAB - Dept. Microelectronics and Electronic Systems

Mercè Villanueva. Degree in Mathematics. PhD in Computer Science. UAB - Information and Communications Engineering. Group researcher Combinatorics, Coding and Security Group (CCSG). Area of ​​coding optimization in Digital Transmissions and Data Storage applied to the information society.

Registration

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.

Members of the Parc Taulí University Hospital, Bioinformatics Barcelona and UAB Alumni will be given a 10% discount.

Steps to follow to formalize the registration:

1. Send your details to the Technical Secretariat

    First and last name

    DNI

    Address

    Postal code

    Population

    Email

    Profession

    Work placement

    Modality of training

    2. Send the documentation

    The documentation that must be submitted is:

    • University degree, with original compulsion.
    • Photocopy of ID

    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: 5.700 €
    • Postgraduate Diploma: 2.850€
    • Course: 950€

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

    SABADELL BANK
    ES69 0081 5154 22 0002103622

    Once you've made your payment, you'll need to email (efreixa@tauli.cat) 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 efreixa@tauli.cat to provide tax information:

    • Company name
    • Address, postal code and population
    • VAT/Fiscal Code
    • 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)

    Face-to-face classes will be held at Classrooms of the Faculty of Medicine - Teaching Unit Autonomous University of Barcelona of the Parc Taulí.

    Contact

    Direcció

    • Jose Ibeas. Nephrology Service. Parc Taulí University Hospital
    • Javier Serrano. Department of Telecommunications and Systems Engineering. School of Engineering - Autonomous University of Barcelona

    Endorsed by

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

    When you visit our website, your browser may store information about specific services, usually in the form of cookies. Here you can change your privacy preferences. Please note that blocking certain types of cookies may affect your experience on our website and the services we offer.

    Enable / disable Google Analytics tracking code
    Enable / disable Google Fonts
    Turn Google Maps on / off
    Enable / disable embedded videos
    This website uses cookies, mainly from third party services. You can edit your privacy preferences and / or accept the use of cookies.