Project 6: Clinical decision support for precision medicine

Rime Raissouni

Siemens Healthcare GmbH
Advisors: Tobias Heimann & Volker Tresp

Project description

In the scope of the MLFPM research goal “to develop methods for improving medical decision support systems through health record mining”, this individual research project aims to develop a conceptual model of clinical decision processes based on machine learning approaches, which accounts for the need to deal with incomplete data, causal relations between observations and to guarantee a high level of interpretability and acceptance by the medical practitioners. Objectives

  1. Conceptual analysis of meaningful use of clinical and healthcare data. Particular focus on issues of causality and incompleteness of data
  2. Development of decision support tools based on state-of-the art machine learning approaches (e.g. tensor memory models, representation learning, deep neural networks)
  3. Conceptual analysis and development of solutions for clinical employment of the developed solutions with a particular focus on workflow and acceptance by decision makers.


  1. Integrating omis-data into a conceptual model of clinical decision processes
    Host: FPS
    Planned date: November 2020 – January 2021 Postponed due to Corona
  2. Therapy outcome prediction in the presence of massively missing data
    Host: Armines
    Planned date: December 2021 – February 2022