Basic Information

Seminar Description

Health-care has turned into a big data-driven domain, which produces a multitude of data, such as structured data in the form of clinical health records, free text in form of case studies and clinical trials, or colloquial discussions shared via social media outlets or patient forums. While this data overload can be a challenge for patients and caretakers, it also enables Machine Learning (ML) approaches to deliver new insights and open new application opportunities. For instance, it enables physicians to retrieve relevant information about disease outbreaks, viruses, etc substantially faster or it facilitates the discovery of phenotype-specific relationships between diseases and drugs to develop personalized medicine. This seminar covers a range of topics, showcasing the application of ML to such use cases. Some relevant topics include: Information Retrieval & Extraction, Conversational Health AI, Health Data Science etc.



Rules and Grading

  • Every student have to choose 3 preferential topics of which he/she will get one assigned.
  • At the end of the seminar every student needs to submit one technical report of 8 pages. 
  • The student will be evaluated based on the submitted reports and an oral presentation.