Informatics and Applications of Medical Informatics


This curricular unit aims to promote the use of advanced methodologies in data analysis and image processing using Artificial Intelligence techniques. These methodologies are relevant in pattern recognition, as well as in supporting clinical decision-making based on machine learning from examples and recorded experiences.

For a proper use of these methodologies, is organized a process for knowledge discovery in Databases (KDD). This process begins with the selection and organization of clinical data in a database (e.g. Microsoft Access). At this stage the student will develop the skills in building, organizing and consulting a relational database.

A second stage will focus on data modeling using machine learning methodologies using free software packages based on the R language and implementing the automatic learning methodologies in this platform. The models to be implemented are mainly based on regression trees and artificial neural networks, which will be used for the analysis of clinical data including medical image processing.

General characterization





Responsible teacher

Prof. Doutor Carlos Geraldes


Weekly - Available soon

Total - Available soon

Teaching language





Microsoft Access 2013, Mário Paulo Teixeira Pinto, ISBN 978-989-615-188-1, Out/2013, Editora Centro Atlântico;

 Machine Learning with R, Brett Lantz, ISBN 1782162143 9781782162148, 2013, Packt Publishing;

Teaching method

Teaching is mostly practical: resolution of practical exercises based on construction of relational databases (in MS Access) and Artificial Intelligence methods to model the data. Interaction between students and teachers is both at the classroom and by e-mail.
Classes will take 120 or 240 minutes. In this last case, a 30-minute break will be taken. Classes will take place at a classroom with computers (1 for each student).

Evaluation method

The assessment process is based in a continuous approach by following up student’s knowledge and abilities, the class attendance and the frequency of his participation in classes. In addition, a formal written examination and a group project will take place. The teaching evaluation will be done by the anonymous and voluntary response of a questionnaire, aiming to collect the opinion concerning the training objectives, syllabus, evaluation methodology, integration of the different themes, as well as the quality and performance of the different teachers.

Subject matter

Introduction to relational databases. Design of the conceptual model of a database using the Entity / Relationship diagram. Construction of the logical model. Building the physical model and implementing the database, building forms and queries through Microsoft Office Access. Introduction to R language, automatic learning methods in Artificial Intelligence, algorithm performance evaluation methods, introduction to image processing based on deep learning methods.


Programs where the course is taught: