The Text Mining course is an elective course within the eXplainable Artificial Intelligence in healthcare Management (xAIM) master’s programme. As Artificial Intelligence (AI) becomes increasingly important, especially within the healthcare sector, it is becoming crucial to address the lack of digital skills training within the sector. This master’s programme seeks to address this by training qualified healthcare professionals in the field of AI and computer scientists in the field of healthcare.

Text Mining: Learning outcomes

In the Text Mining course, the student will acquire knowledge on the use of the core machine learning algorithms for text mining. With this course, students will be introduced to natural language processing, text mining, and text analysis. They will learn to accomplish various text-related data mining tasks through visual programming. After the completion, students will be able to preprocess textual data, understand specifics of text, transform raw text to attribute-value representation and evaluate language-based models. 

Lecture 1: Introduction to Text Mining

The first lecture introduces text mining and is divided into three chapters:

• What is text mining? an overview of extracting insights from textual data and its applications, particularly in healthcare.
• Text representation: key methods for converting text into machine-readable formats, such as Bag-of-Words and TF-IDF.
• Practical applications: examples include sentiment analysis, document classification, and topic modeling.

Each chapter combines theory with practical examples for hands-on learning. The chapters are prepared by Ajda Pretnar Žagar and Blaž Zupan with the support of members of the Bioinformatics Lab at the University of Ljubljana in Slovenia.