Artificial Intelligence Topics in Medicine: Pattern Detection

Recommended Prerequisites

NA

Teaching Methods

Expository, exemplary, followed by problem solving.Teaching methods are sundry and adapted to the requirements and goals of the discipline. The aim is to develop the ability for data evaluation by choosing the most appropriate statistical procedures for each situation.

Initially, the application of these methods in the medical field is contextualized and the fundamental notions are explained. Subsequently, they are applied with practical real examples. The use of computer tools allows the apace obtention of results, placing the focus on their interpretation and analysis. 

Learning Outcomes

The student must be able to distinguish between the main sytatistical techniques that are available to help the clinical diagnosis and assist the patients prognosis automatically, and how he/she may improve them in order to validate them while they learn from misclassification or misprognotics.

Work Placement(s)

No

Syllabus

This discipline seeks to explore the multivariate character of most problems of diagnosis and clinical prognosis. Using the fundamentals of the statistical classification and regression analysis, the automatic classification methods and forecasting models will be applied as a tool to support diagnosis and clinical prognosis, in an approach in which the gold standard for health status is known (supervised learning). The examples discussed will be based on real medical problems, already published. Students will solve similar problems with data randomly generated from real data.

Head Lecturer(s)

Bárbara Cecília Bessa dos Santos Oliveiros Paiva

Assessment Methods

Assessment
Laboratory work or Field work: 100.0%

Bibliography

- Aviva Petrie and Caroline Sabin. Medical Statistics at a Glance. John Wiley and Sons.

- David Collet, Modelling Survival Data in Medical Research. Boca Raton: Chapman & Hall/CRC.

- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.

- Wernecke KD. On the Application of Discriminant Analysis in Medical Diagnostics. In: Bock HH., Lenski W., Richter M.M. (eds) Information Systems and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer,