LO1.Understand the principles of health data collection, management, and analysis.
LO2. Analyze the role of health information systems in healthcare delivery.
LO3. Learn about data standards, data sources, and data analytics tools used in healthcare.
LO4. Develop an understanding of the ethical and legal considerations related to health data and information systems.

CP1.Introduction to Health Information Systems and Data: Overview of health information management. Importance of health data for healthcare provision and decision-making. Introduction to health information systems and electronic health records (EHRs)
CP2: Health data collection and standards.
CP3. Data interoperability in healthcare.
CP4. Health data management and analysis.
S5. Clinical decision support systems (CDSS)
CP6. Implementation of Health Information Systems: Selection and acquisition of systems. System implementation methodologies.
Change management and user adoption strategies.
CP7. Future trends in health data and information systems: Big data and analytics in healthcare. Artificial intelligence and machine learning in health data analysis. Personalized medicine and precision computing in healthcare.

The teaching-learning methodology of the UC is active. The student is the main responsible. There is an incentive to develop the capacity to absorb content autonomously, through the analysis of literature and practical cases, use and critical discussion of digital content, thus mixing theoretical content taught in personal participation, and individual and group projects. In theoretical classes, structural and conceptual bases are presented and the work is aligned with the learning objectives. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

LO1: Explain and understand the concept of digital transformation and its significance in healthcare management.
LO2: Identify and evaluate key technologies driving digital transformation.
LO3: Analyze the impact of digital transformation on healthcare delivery, patient care, and organizational processes.
LO4: Apply principles of health informatics to support data-driven decision-making in healthcare organizations.
LO5: Assess the opportunities and challenges associated with electronic health records implementation.
LO6: Describe the role of telemedicine in improving access to healthcare services and patient outcomes.
LO7: Utilize data analytics for informed decision-making.
LO8: Discuss the ethical and privacy considerations related to the use of digital technologies in healthcare.
LO9: Develop strategies for effectively engaging patients in their healthcare through digital tools and platforms.
LO10: Design and propose a digital transformation plan for a healthcare organization.

Course Topics:
PC1: Introduction to Digital Transformation in Healthcare Management
PC2: Health information systems and interoperability
PC3: Telehealth technologies and applications
PC4: Data-driven decision-making in healthcare
PC5: Importance of patient engagement in healthcare
PC6: Managing digital transformation projects
PC7: Privacy and security of healthcare data
PC8: Analysis of successful digital transformation initiatives in healthcare organizations
PC9: Data analytics exercises using healthcare datasets
PC10: Blockchain and its applications in healthcare
PC11: Addressing interoperability challenges

The course will be delivered through a combination of lectures, case studies, discussions, and student-led presentations. Students will be required to complete individual assignments and a group project that involves designing an interoperability solution using emerging technologies. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Analyzing the underlying needs of a healthcare technology problem and suggesting standards to address gaps and provide solutions in healthcare technology applications. Explaining the difference between standards, guidelines, and protocols. Exploring ways to develop or utilize technological systems and/or methodologies to apply standards. Understanding the concepts of data classification and its significance in healthcare. Evaluating different data classifications, terminologies, and standards. Proficiency in using widely adopted health data classifications. Explaining the principles of standardized data exchange formats. Identifying the challenges and opportunities associated with interoperability, reuse, and exchange of health data. Analyzing real-world scenarios to determine data classifications and exchange formats. Applying best practices in data classification and exchange to enhance data management and analysis, as well as business development.

1: Introduction to digital health trends and standardization: Data coding, definitions, and classifications. Terminologies and vocabularies.
2: Common data classifications: International Classification of Diseases (ICD). Systematized Nomenclature of Medicine (SNOMED CT). Logical Observation Identifiers Names and Codes (LOINC). International Classification of Health Interventions (ICHI). International Classification of Functioning, Disability, and Health (ICF). Anatomical Therapeutic Chemical Classification System (ATC) and Defined Daily Dose (DDD).
3: Data exchange formats: Health Level Seven (HL7). Fast Healthcare Interoperability Resources (FHIR). Clinical Document Architecture (CDA).
4: Data standards and reuse: Data interoperability challenges. Primary and secondary uses of data. Actors, constraints, and challenges.
5: Case studies and applications.

In general, students will complete assignments that involve analyzing and applying health data classifications and exchange formats to real-world scenarios. Online Quizzes: Regular online quizzes can be used as a formative assessment method to evaluate students' understanding of key concepts and principles.
Students will analyze case studies to demonstrate their understanding of health data classification and exchange. Case Study Analysis: Assigning case/use studies can assess students' ability to apply their knowledge and skills in real-world scenarios.
Project: Students will work on a project to propose and implement appropriate health data classifications and exchange formats for a specific healthcare setting.
Group Project: Engaging students in a collaborative group project can assess their ability to work in teams and apply their knowledge and skills collectively. More specifically, an inter-disciplinary project involving the different contexts (data science, social and business health contexts) in topics of health data re-use is a niche idea. Examinations: Periodic examinations will be conducted to assess students' knowledge of the course material. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

After completing the curricular unity the student is able to (LO = learning outcomes)
LO1 identify possibilities and limitations of digitality in individual health decisions
LO2 understand the relevance of regulation and other public policy tools in health promotion
LO3 assess individual behaviour and evaluate the prerequisites for behavioral change
LO4 argue the relevance of behavioral insights in developing digital solutions
LO5 design interventions that promote behavioural change
LO6 estimate the impact of behavioural change interventions at the levels of health and financial benefits

S1 Social and individual approach to the digital transformation in the health sector
S1.1 Role and limitations of digitality in individual health
S1.2 Regulation and other public policy tools related to public health
S2 Applying behavioral insights in health care and health promotion contexts
S2.1 Development and evaluation of digital solutions and real life behavioral interventions
S2.2 The cognitive biases and heuristics in understanding patients and service providers behaviour
S3 Behavioral change principles and interventions as means to improve health outcomes
S3.1 Governmental interventions and policies in promoting public health
S3.2 Health care professionals role in assisting patients in forming healthy habits
S4 Impact assessment of behavioral interventions
S4.1 Evaluation of the behavioral change and the related health benefits
S4.2 Financial impact and cost effectiveness of the interventions

Study Units are evaluated with points. Students need to get at least 50% of the maximum points to pass the Study Unit. Each university will change the points to the grades based on their grading system. Group assignments (90%) and individual assignments (10%). This CU includes also assignments that are graded with fail/pass. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Knowledge: Understand the concepts and principles of systemic service design. Acquire a comprehensive understanding of the applicability of service design in healthcare through online studies, interactive lectures, case studies, group discussions, and hands-on experiments.
Skills: Acquire skills to apply service design in practice to enhance the systemic development of the social welfare and healthcare service ecosystems.
Competencies: Acquire general knowledge of human-centred and systemic service design in the social welfare and healthcare service ecosystems.

"Pre-assignment (reading, writing key insights on a discussion forum)
Online kick-off (online 2 hours)
Desk Research and stakeholder interviews (remote work between the kick-off and 5-day sprint, individual and teamwork)
5-Day Co-creation Sprint Week held annually in ManagiDiTH partners' countries about once a year
1. Problem definition
2. Ideation and sketching
3. Deciding and iterating
4. Prototyping and piloting
5. Showcasing results
End Gala Presentations

Note: Students who are unable to participate in the 5-day sprint on-site form their team and do their assignments online and/or in their local community. The supportive activities and coaching that are part of the 5-day sprint are organised as asynchronic and synchronic online activities. This has to be planned in more detail."

Study unit is evaluated with points (maximum 100). Students need to get at least 50 points (50%) to pass the study unit. Each university will change the points to the grades based on their grading system. Assessment: Individual pre-assignment (pass/fail), online Quizzes individually (10%), service design case in teams (90%). The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

"The student is able:
_to describe national and international health and wellbeing policies and models in local contexts; _to critically analyse and apply knowledge about health and wellbeing regulations, agreements, funding instruments and bodies; _to recognize the needs for multi-professional and cross-cultural health care and social wellbing service development; "

The course starts with a pre-assignment in which students analyze and describe their home country's health and wellbeing systems. The joint sessions start with a flipped classroom approach, in which students will present their findings in class. During joint sessions, students will then discuss key differences and similiarities between countries. The course also includes lectures by external professionals within the health and wellbeing sector. During the course, students will do a project in groups. This group work project consists of an interview with a health and wellbeing sector professional, with the aim of mapping challenges and possibilities related to digitalization. he interviewee can a person be from the public, private, or third sector in the health sector. In an individual assignment, students write an essay where they synthesize their learning. The essay includes an assessment of how different health and well-being systems can respond to different health objectives.

Students will be assessed based on individual (60%) and group (40%) assignments. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Knowledge: Understand the concepts and principles of resource management in healthcare through interactive lectures, case studies, and group discussions, enabling in-depth exploration and application of theoretical frameworks and practical examples.

Skills: Develop proficiency in applying analytical techniques for resource allocation and budgeting via hands-on exercises, simulations, and real-world scenarios. Engage in data analysis, modeling, and decision-making to enhance practical skills and critical thinking.

Competencies: Create strategic plans for resource optimization in healthcare by using case studies, group discussions, and practical exercises. Apply critical thinking to analyze complex scenarios, identify efficiency opportunities, and design strategies aligned with organizational goals, fostering creativity and innovation while considering constraints and ethical issues.

Introduction to health resource management
Healthcare financing and budgeting
Understanding the financial aspects of healthcare and mastering budgeting strategies for sustainable healthcare systems
Resource allocation strategies
Inventory management and supply chain optimization
Enhancing inventory management and optimizing supply chains to ensure efficient resource utilization in healthcare settings
Human resource management in healthcare
Developing effective human resource management strategies to optimize healthcare workforce performance.
Technology utilization for resource efficiency
Exploring innovative technologies to enhance resource efficiency in healthcare operations.
Quality improvement and cost-effectiveness
Implementing quality improvement strategies to achieve cost-effective healthcare outcomes.
Ethical considerations in resource allocation
Exploring ethical dilemmas and decision-making in the equitable allocation of limited healthcare resources.

Online Quizzes: Regular online quizzes can be used as a formative assessment method to evaluate students' understanding of key concepts and principles in resource management.
Case Study Analysis: Assigning case studies related to resource management in the health sector can assess students' ability to apply their knowledge and skills in real-world scenarios.
Group Project: Engaging students in a collaborative group project focused on resource management can assess their ability to work in teams and apply their knowledge and skills collectively. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Student will:
LO1: - analyse the different models of wellbeing production
LO2: - analyse how well-being and its promotion and prevention are reflected in the values of EU and at the level of fundamental rights in the EU and its member states.
LO3: - compare the differences in the national and Eu-level regarding the structures of EU policy making and regulations in order to understand EU- Health and Wellbeing digitalisation regulatory framework.
LO4: investigate the EU- Health and Welbeing digitalisation regulatory framework´s from perspectives of the public and private health and wellbeing services providers, third sector and health technology companies in in order to get knowledge how to make change together in digital transformation.
LO5: formulate and describe concrete steps to how to bring the digital health and wellbeing application in to use.

PC1: Different models of well-being production, including institutional, neoliberal, populist, and residual welfare state regimes.
PC2: Various visions of digital transformation in health and well-being.
PC3: Examples of product implementation and roadmaps for digital service pathways.
PC4: Well-being, its promotion, and prevention as a concept.
PC5: EU values and fundamental rights within the EU and its member states.
PC6: National and EU-level structures for EU policy-making and regulations.
PC7: Regulatory frameworks for EU health and well-being digitalisation.
PC8: EU health and welfare digitalisation regulatory frameworks.
PC9: Perspectives of public and private health and well-being service providers, the third sector, and health technology companies on collaborative change in digital transformation.
PC10: Concrete steps for implementing digital health and well-being applications, considering existing regulations and institutional structures.

The study unit is evaluated with points (maximum 100). Students need to get at least 50 points (50%) to pass the study unit. Each university will change the points to the grades based on their own grading system. The evaluation grading is based on home univesity crading. Assessment: exercises / problems performed in group, during the course classes (30%), and an group assignments in the performed at the end of the course (70%).
The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

By the end of the course students will be able to: -understand the basic principles and parameters for business planning in healthcare; -define and identify innovation; -perform/conduct small scale research re professional development opportunities for MDs; -use tools for business planning; -appreciate the role and importance of the entrepreneurship lab -adopt best practices in advancing entrepreneurship -collaborate with other disciplines -evaluate different business initiatives -compare good and bad practices of different enterprises -prepare modern presentations on business planning

Introduction: Course overview, objectives, and expectations. Importance of managerial and entrepreneurial skills in healthcare

Innovation Theory and Practice: Theoretical foundations of innovation in healthcare. Strategies for fostering and managing innovation in healthcare organisations

Business Setting and Digital Marketing: Digital marketing and effective management techniques for healthcare enterprises.

Pitching: Developing presentation skills for effective pitching

Social Innovation: Exploring social innovation initiatives in healthcare.

Social Enterprises: Analysing successful social enterprise models in healthcare.

Cases and Exercises: Analysing real-world cases and exercises in healthcare management.

Business Planning Labs: Practical workshops for developing comprehensive business plans. Applying business planning methodologies and tools in healthcare.

Presentation of the Business Plan: Presenting and defending business plans. Feedback and opportunities for improvement.

The assessment of students includes the evaluation of a group-based business plan, which serves as a comprehensive measure of their understanding and application of the course concepts.
The assessment methods utilized are a report and a laboratory assignment. The report serves as both a formative and summative assessment, allowing students to demonstrate their knowledge, analysis, and critical thinking skills in presenting their business plan. The laboratory assignment provides a formative assessment opportunity, allowing students to apply their learning in a practical setting and receive feedback for improvement. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

LO1: Understand the philosophical and theoretical foundations of ethics in the health field.
LO2: Acquire knowledge of specific ethical indicators and frameworks in the health field.
LO3: Develop skills for analysing and resolving ethical dilemmas in the healthcare field.
LO4: Understand the principles and ethical practices of scientific research involving human subjects in the health field.
LO5: Understand and apply regulatory frameworks for privacy and data protection in the health field, particularly the GDPR.
LO6: Analyse the ethical implications of emerging technologies and innovations in the healthcare field, such as artificial intelligence, data science, and robotics.
LO7: Develop critical and ethical thinking skills to address ethical dilemmas and make informed decisions in complex situations in the healthcare field.

PC1: Ethics in the Health Field
Ethics, conduct, and integrity in healthcare.
Ethical theories and moral decision-making: virtue ethics, utilitarianism, deontology, and principles approach.
Ethical issues and dilemmas in healthcare.
Codes of ethics and responsibilities of healthcare professionals.

PC2: Ethics and Research in the Health Field
Research involving human subjects: Belmont Report, WMA Declaration of Helsinki, principles, and practical guidance.
Clinical trials regulation in the EU and Portugal.

PC3: Privacy and Data Protection in the Health Field
GDPR: principles, definitions, lawfulness of processing, special categories of personal data, data subject rights.
Data processing for health, safety, and scientific research.
Anonymisation and pseudonymisation techniques.

PC4: Ethical Issues of Digitalization, Data Science, and AI in Healthcare.

PC5: Digital Health Policies in Portugal and the European Union.

1. Class participation and discussion: 20%
2. Assignments and projects: 50%
3. Midterm exam: 15%
4. Final exam: 15%

"Understand the principles of health and wellbeing, equal access to health, digital literacy and capabilities that digital healthcare could bring, global digital divides, institutional challenges of global, European, national and local levels in health service provision, health rights and power relations - with, for and by the people;
Acquire general knowledge of philosophical, historical and recent transformations in the world of health, particularly in relation with the development of the digital society;
Build a multidimensional understanding of the concepts of health (mental health, cultural lifestyles), society (health society, common people, care) and technology (common good);
Apply critical and ethical thinking related to the development of health technologies;
Identify and analyze complex and heterogenous data linking health differences, quality of life and social wellbeing, ownership and control of health data;
Reconstruct the content of a resource and discuss it with the class"

Introduction to Science and Technology Studies (STS)
Course overview: Presentation of key STS concepts such as symmetry, sociotechnical controversies, and the social construction of technology.
Knowledge production in medicine: Examine transformations from the 1970s-80s, including molecularization, biotech advances, clinical trials, and genomics.
Medical expertise: Explore the evolution of medical expertise, including the rise of agencies, medical activism, and knowledge co-production.
E-health worlds: Understand the development, politics, and impact of e-health, focusing on the Global South.
Big Data and Medicine: Define medical data and explore issues related to data politics, innovation, and labor transformation.
Gender and Medicine: Investigate gender inequalities and the role of science in gender assignment.
Technoabilities: Explore technology’s role in disability.
Restitution: Present and discuss student projects.

Online Quizzes: Regular online quizzes can be used as a formative assessment method to evaluate students' understanding of key concepts and principles in resource management.

Reading, summarizing and illustrating a scientific article. Each session will be introduced by a group of students responsible for presenting a key text for the session and illustrating it with a concrete case study.

Group Investigation: Engaging students in a collaborative group investigation focused on analyzing a medical controversy or studying the socio-technological trajectory of a medical device or therapy.

The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

LO1 Demonstrate a comprehensive understanding of principles, concepts of interoperability in healthcare
LO2 Identify the key components of interoperable health systems
LO3 Explore technologies to achieve interoperability in healthcare (HL7, FHIR, DICOM, and APIs)
LO4 Analyze the application of available technologies in different healthcare settings
LO5 Understand how to design and apply interoperable healthcare systems, including data exchange protocols, security and privacy features
LO6 Explore the basics of the testing continuumto assess Interoperable Ecosystems
LO7 Learn how to apply knowledge and skills to different scenarios
LO8 Understand how to identify and address the regulatory requirements for interoperability
LO9 Understand the importance of data security/privacy
LO10 Engage in lifelong learning
LO11 Understand the concepts of an interoperability framework and governance
LO12 understand the necessity of interoperabiliy specifications and the use case methodology

PC1: Introduction to Interoperability in Healthcare
PC2: Healthcare Interoperability Standards and Protocols (HL7, IHE tools, prototypes)
PC3: Nomenclatures and Terminologies - data sets (EU experiences)
PC4: International Experiences, Best Practices of Interoperability in Healthcare (present country cases, regional cases, etc.)
PC5: Interoperability Implementation: testing, conformity assessment
PC6: My Health@EU Primary Use Interoperability Requirements
PC7: My Data@EU Secondary Use Interoperability Requirements
PC8: Past & Active European Relevant Initiatives
PC9: Blockchain Technology in Interoperability
PC10: AI Technology in Interoperability
PC11: IoT Technology in Interoperability
PC12: Ethical, Legal, and Policy Issues
PC13: Challenges and Opportunities of Interoperability in Healthcare (Governance Issues, etc.)

Online Quizzes: Regular online quizzes can be used as a formative assessment method to evaluate students' understanding of key concepts and principles.
Case Study Analysis: Assigning case/use studies can assess students' ability to apply their knowledge and skills in real-world scenarios. Assessment: exercises / problems performed in group, during the course classes.
Group Project: Engaging students in a collaborative group project can assess their ability to work in teams and apply their knowledge and skills collectively. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

At the end of the learning unit, the student must be able to:
LO1: Understand the unique cybersecurity challenges in healthcare and the importance of protecting sensitive patient data.
LO2: Develop the knowledge and skills to assess and manage cybersecurity risks in health systems.
LO3: Implement security measures to safeguard network infrastructure, applications, and medical devices.
LO4: Ensure compliance with data privacy regulations and employ techniques to protect patient confidentiality.
LO5: Establish effective incident response and disaster recovery plans to mitigate and recover from cybersecurity incidents.
LO6: Stay updated on emerging trends and technologies in healthcare cybersecurity, while considering ethical considerations and industry best practices.

S01. Introduction to Cybersecurity in Healthcare
S02. Security Fundamentals for Health Systems
S03. Risk Assessment and Management in Health Systems
S04. Network Security in Health Systems
S05. Application Security in Health Systems
S06. Data Privacy and Confidentiality in Health Systems
S07. Incident Response and Disaster Recovery in Health Systems
S08. Threat Detection and Prevention in Health Systems
S09. Secure Cloud Computing in Health Systems
S10. Medical Device Security
S11. Security Governance and Compliance in Health Systems
S12. Emerging Trends and Future Directions in Healthcare Cybersecurity

Periodic Assessment:
- Group labs (50%) (max. 3 students per group) [minimum score of 10 points for each lab]
- Two individual tests (50%) [minimum score of 6 points for each test] (1 test during the semester, the other on the 1st season date)
Students should attend a minimum of 50% of classes to be able to do the periodic assessment.
The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Knowledge: Gain a thorough understanding of e-health and telemedicine principles and applications in healthcare, including the technologies and systems used for remote healthcare delivery.
Skills: Develop practical skills in using digital technologies and telecommunication systems for effective remote healthcare. Learn how to apply these tools for virtual consultations, remote monitoring, and other telehealth interactions.
Competencies: Build the ability to analyze and address challenges and ethical considerations in e-health and telemedicine. Learn to critically evaluate privacy, security issues, and ethical dilemmas in digital healthcare.

By achieving these outcomes, students will acquire the knowledge, skills, and competencies needed to navigate and contribute to the evolving field of e-health and telemedicine, addressing both ethical and practical challenges in remote healthcare.

Introduction to E-Health and Telemedicine: Overview of e-health and telemedicine, their benefits, and challenges in healthcare.

Telecommunication Systems in Healthcare: Covers systems for remote healthcare such as teleconsultation, telemonitoring, and teleconferencing.

Digital Health Technologies: Examines electronic health records, mobile health apps, wearable devices, and their effects on healthcare delivery.

Legal and Ethical Considerations: Discusses privacy, security, and regulatory issues in e-health and telemedicine.

Implementing E-Health and Telemedicine: Looks at infrastructure needs, deployment considerations, and system interoperability.

Case Studies and Real-World Examples: Offers insights through case studies and real-world applications of e-health and telemedicine.

The assessment for the curricular unit "E-Health and Telemedicine" will employ various methods to evaluate student learning. Written examinations will be used to assess knowledge and understanding of the principles and applications of e-health and telemedicine. Practical assignments will test students' skills in utilizing digital technologies and telecommunication systems for remote healthcare delivery. Additionally, case studies or projects will provide opportunities for students to demonstrate their competencies in analyzing and addressing challenges and ethical considerations in the field. By employing a diverse range of assessment methods, students' comprehensive understanding, practical skills, and critical thinking abilities in e-health and telemedicine will be effectively evaluated.
The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

The Learning Objectives (LO) of the Study Unit:

LO1: Apply and understand various stages within the realm of machine learning.
LO2: Recognize and locate crucial data points.
LO3: Employ imputation techniques for data replacement and establish appropriate metrics.
LO4: Identify and employ supervised and unsupervised algorithms suitable for health data analysis.
LO5: Evaluate and interpret the performance of the various machine-learning algorithms on health data.
LO6: Implement a machine learning pipeline in an ML toolkit.

CP1: Foundations of AI in Health (1 ECTS)
• Introduction to Health Informatics and its significance in healthcare
• History of Artificial Intelligence
• Basics of Artificial Intelligence and Machine Learning
• Ethical and Regulatory Considerations in AI for Health
CP2: Setting up the working Environment (1 ECTS)
• Creation of Working Directories
• Install the tool Orange.
• Install BERT (R Excel Toolkits)
CP3: Data Pre-processing (1 ECTS)
• Data Collection
• Data Preparation
• Data Cleaning
• Data Validation
CP4: Machine Learning Algorithms (1 ECTS)
• Supervised, Unsupervised, and Reinforced Learning
• Evaluation Metrics
• Utilisation of the dataset in the tool Orange
CP5: Applications (2 ECTS)

"Evaluation Parameters:
Total: 100 points

Assignment 1: Write a report on the basic algorithms used in AI with at least five scientific journal references (20 points)

Assignment 2: Extract some statistical features from the data and do some visualization of the data (20 points)

Quiz 1: A quiz to check the basics of AI (10 points)

Quiz 2: To Check the knowledge of the algorithms from different use case scenarios. (10 points)

Project Completion: (40 points)

Open Discussion: About a notion of application of ML in the healthcare sector

The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

General Information on sensors and Instrumentation
General knowledges concerning sensor classification, principles and application, Sensors conditionning and Instrumentation amplifier

Sleep pathologies
Understanding sleep disorders, biomedical sensors to perform a sleep exam

Instrumentation for Electrophysiological measurements
General knowledge on neurons and electrophysiology, Instrumentation associated, Basic Signal treatment for ECG/EMG/EEG.

Miniaturization and integration
Knowledge in sensors miniaturization, Microfluidics and Lab-On-a-Chip: context and market

Monitoring electrical properties of living one
Understanding Bioimpedance, Use of Bioimpedance to monitore physiological state

Synthesis and Oral Defense
Analysis of a topics relative to sensors for medical instrumentation: bibliography analysis for a short synthesis (report writting) and oral defense (short video recording).

General Information on sensors and Instrumentation (8 hours)
Sleep pathologies (8 hours)
Instrumentation for Electrophysiological measurements (8 hours)
Miniaturization and integration (8 hours)
Monitoring electrical properties of living one (8 hours)
Synthesis and Oral Defense (10 hours)

Online Quizzes : At the end of all chapter of the lectures, multiple choice questions will be used to evaluate student's progress.
Writing exam : Two writing exam (mid-term and end) will be plan with a view to developing reasoning skills and highlight on-line lab work.
Personal/group work : a short report an associated oral presentation (video) will conlude the lectures. This personal work will be avaluate by a peer review. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

LO1: To represent an image in different color spaces and in the frequency domain
LO2: To perform typical image processing operations
LO3: To extract low-level characteristics from an image
LO5: To implement an automatic learning system based on classic algorithms for image content classification
LO5: To know the typical architecture of a convolutional neural network (CNN) and to understand how it works
LO6: To solve a medium complexity image classification problem using CNNs
LO7: To apply transfer learning / fine-tuning methodologies based on pre-trained CNNs
LO8: To use deep learning algorithms for image objects identification
LO9: To know deep learning algorithms for automatic generation of multimedia content
LO10: To manipulate images using the OpenCV library and use the Tensorflow library to develop automatic learning applications
LO11: Healthcare applications

PC1 Image representation
PC2 Image operations
PC3 Extraction of image features
PC4 Introduction to machine learning
PC5 Artificial neural networks
PC6 Convolutional neural networks
PC7 Transfer Learning
PC8 Network architectures for detecting and identifying image objects
PC9 Network architectures for automatic content generation
PC10 Developed Health Care Applications

Theoretical-practical classes that alternate exposition and application moments, with examples and exercises that involve the development of code snippets using the OpenCV and Tensorflow libraries. The course follows a project-based continuous assessment model throughout the semester due to its highly practical nature, and does not include a final exam.

Acquire the ability to carry out an applied project autonomously.
Select one or more methodological approaches to carry out the project.
Know how to validate the artefacts that constitute the solution to the chosen problem and identify the corresponding validity threats.
Have learned about the complexity of preparing a successful, high-quality master's thesis, both in form and content.
Be able to present an applied problem, defining objectives and their motivation in order to produce appropriate and validated solutions.

"The work program is based on a topic of intellectual interest to the student, which will be approached according to a personalized work program to be agreed with the supervisor. However, the work to be carried out must materialize in a written document based on a pre-defined template containing:
1. The formulation of a question or problem (identification of objectives), theoretically susceptible of obtaining an adequate answer through the mobilization of a scientific research methodology.
2. Definition of the requirements and initial starting conditions.
3. The work must respond to a practical need in the area of health.
4. Finally, the project must contain a concluding summary that responds to the objectives identified, as well as suggestions for future research."

The evaluation of the CU process will be through the public discussion of the dissertation presented by the student, conducted by a panel.
The final rating (0 to 20) will be assigned by the panel, given the academic quality of written work presented (especially the relevance, originality and consistency of theoretical and methodological shown), as well as the student's performance during the presentation and discussion of the text.

"Acquire the capacity to undertake research independently. Know how to develop a review based on relevant literature in a given-scientific field
Select one or more methodological approaches to achieve the project.
Know how to validate the artifacts that constitute the solution to the chosen problem and identify the corresponding validity threats.
Have learned about the complexity and how to prepare a successful master dissertation with high quality, both in form and content.
To be able to present a technical-scientific problem and its motivation, to produce appropriate and validated solutions."

"The work program starts from a topic raised by the student's intellectual interest, a topic that will be addressed according to a customized program of work to be agreed with the supervisor. Notwithstanding this, the work to be done must materialize in a ""paper"" containing:
1.The formulation of a question or a problem, theoretically capable of having an appropriate response through the mobilization of scientific research methodology.
2.A review of the theoretical issues underlying the question above, obtained through research, analysis and critical interpretation of the latest internationally accepted scientific literature.
3.In coordination with the earlier theoretical balance, the dissertation must contain an exercise (theoretical and / or empirical) that complements an innovative way to approach the topic under investigation.
4.Finally, the dissertation must contain a conclusive synthesis answering the research starting point, as well as suggestions for further research"

"The evaluation of the CU process will be through the public discussion of the dissertation presented by the student, conducted by a panel.
The final rating (0 to 20) will be assigned by the panel, given the academic quality of written work presented (especially the relevance, originality and consistency of theoretical and methodological shown), as well as the student's performance during the presentation and discussion of the text."