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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Yunus Emre Health Services Vocational School
  2. Department of Database, Network Design and Management
  3. Program in Digital Health Systems Technician
  4. Course Structure Diagram with Credits
  5. Natural Language Processing and Clinical Text Mining
  6. Learning Outcomes & Program Qualifications
  • Description
  • Learning Outcomes
  • ECTS Credit Load
  • Course's Contribution to Program
  • Learning Outcomes & Program Qualifications
Last Updated: 21/08/2026
0 : Does not support   1 : Low-level support   2 : Mid-level support   3 : Top level support
Learning Outcomes Program Qualifications
PY1 PY2 PY3 PY4 PY5 PY6
LO-1 Explain the basic concepts and application areas of natural language processing. - - - - - -
LO-2 Apply text preprocessing techniques (tokenization, stemming, stop-word removal). - - - - - -
LO-3 Describe the structure of clinical text and the characteristics of free-text data. - - - - - -
LO-4 Extract clinical concepts using named entity recognition (NER). - - - - - -
LO-5 Extract diagnosis, medication, and symptom information from clinical text. - - - - - -
LO-6 Explain text classification and sentiment analysis methods. - - - - - -
LO-7 Compare rule-based and statistical approaches in clinical text mining. - - - - - -
LO-8 Explain the application of deep learning-based language models to clinical text. - - - - - -
LO-9 Interpret the process of converting free text into structured data. - - - - - -
LO-10 Evaluate data privacy and ethical issues in NLP applications. - - - - - -

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