ISLA Santarém 26701

Health Decision Support Systems

• ApresentaçãoPresentation

    
• ProgramaProgramme

  1. Introduction to Decision Support Systems in healthcare. 2. Fundamentals of data model design. 3. Business Intelligence (BI) systems architecture 3.1. ETL: Data import, transformation, and loading. 3.2. Creation of Data Warehouses. 3.3. OLAP and Data Mining. 4. Business Intelligence trends in healthcare. 5. BI tools applied to healthcare.
• ObjectivosObjectives

  O1. Present the basic concepts of decision support systems.  O2. Characterize the architecture of BI systems. O3. Characterize Data Waterhouse and OLAP and Data mining tools. Competences: C1. Use software techniques and tools to support decision-making in healthcare. C2. Select and use the most appropriate technique and tool to build a data model suitable for each situation in the healthcare sector.
• BibliografiaBibliography

  Bhatia, Parteek (2019). Data Mining and Data Warehousing, Principles and Practical Techniques, Cambridge University Press. Kantardzic, M. (2020). Data Mining: Concepts, Models, Methods, and Algorithms. Wiley-IEEE Press. Munoz, M. (2019). Global Business Intelligence. Routledge. Santos, M. Y., & Ramos, I. (2020). BIG DATA - Concepts, Warehousing, And Analytics. Routledge.
• MetodologiaMethodology

  Synchronous distance learning: 1. expository method: presentation of each of the content topics, complemented with the demonstrative and interrogative methods to establish interaction with the students through questioning on the themes being analysed and through the resolution of application exercises. Face-to-face: 2. Practical application: through exercises and work aimed at consolidating knowledge. 3. Laboratory practice: based on the Problem-Based Learning methodology (PBL) aimed at finding solutions to problems identified by the students or proposed by the lecturer. Autonomous: 4. Guided research proposed by the teacher. The teacher gives feedback on the development of the problem addressed in the laboratory practice (Tutorial Orientation - OT) via the Moodle teaching/learning support platform.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  5
• NaturezaNature

  Optional
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Avaliação Curricular (contínua):
  A1. Exercícios resolvidos em aula.
  A2. Teste final teórico-prático (individual). A3. Trabalho prático (grupo).
  A classificação final é calculada através da fórmula Classificação Final = A1*0,2+A2*0,4+A3*0,4. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.
  Avaliação Final (A): O estudante realiza o exame teórico-prático (A=100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
  Avaliação em Época de Recurso e Época Especial (A): O estudante realiza o exame teórico-prático (A=100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.