Explainable Artificial Intelligence in Medicine (xAIM) Learning Path - Text Mining

The xAIM project provides a comprehensive learning path designed to equip individuals with the knowledge and skills needed to leverage Explainable Artificial Intelligence (xAI) in healthcare. In collaboration with Goethe University (Germany), Keele University (UK), Leibniz University Hannover (Germany), and the University of Ljubljana (Slovenia), the learning path offers a selected course from the xAIM Master’s program to introduce students to Explainable AI.
The xAIM Master’s program covers core principles across three main areas: healthcare management, artificial intelligence, and ethical and legal considerations. Key topics include the role and applications of AI techniques in the healthcare sector, opportunities and challenges of data-driven approaches in medical environments, methods for analyzing and interpreting complex healthcare datasets and communicating insights to stakeholders, as well as addressing ethical and social implications of AI and new technologies. Additionally, the students develop advanced programming skills, including deep learning, text mining, and computer vision.

Text mining seeks to extract insights, patterns, and knowledge from large sets of textual data, transforming unstructured text into structured information for analysis and decision-making. This curriculum presents a structured learning path that begins with the various techniques for text pre-processing and visualisation, introduces document vectorisation, applies natural language processing approaches to document clustering and classification, and explains topic modelling. It is based on the elective course offered at the xAIM Master of the University of Pavia, developed with co-funding from the xAIM European project.

The course is organised into nine topics, categorised into Introductory (five units) and Advanced (four units). The introductory material covers core concepts of text mining, while advanced units offer further insight into topic modelling, sentiment analysis, keyword extraction, and co-occurrence networks.

Introductory materials

1. Introduction to Text Mining
2. Document Vectorisation
3. Document Classification
4. Document Clustering
5. Topic Modelling

Advanced materials

1. Explaining LDA
2. Sentiment Analysis
3. Semantic Search
4. Document Networks