Streamlining healthcare processes while guaranteeing the quality of care is a prominent item on the political and social agendas, both at the national and European level. In recent years, significant efforts have been made to develop standardized care pathways representing best practices for a number of treatment processes, with the goal of standardizing and improving the quality of care. However, standardized care pathways cannot properly capture the high variability in patients’ characteristics, which often requires tailored adaptations. As a result, decisions related to how to personalize the treatment process for the patient at hand are often almost entirely left to healthcare professionals, with little support.
The PhD project will focus on investigating how the combination of process mining and artificial intelligence techniques can contribute to the personalization of patients’ treatment. Process mining techniques will be employed to gain evidence-based process analytics on treatment processes. These insights will then be used to build a prediction model able to estimate the outcome expected for the current patient. These results will hence be used to build a dynamic recommendation systems to support healthcare professionals in determining the treatment option that best suits the patient’s needs, taking into account multiple aspects related both to the efficacy of a treatment for a given disease and its effects on patient’s quality of life.
You, as a successful applicant, will perform the research project outlined above in an international research team. You will report research findings at international conferences and workshops, and in high-quality scientific journals. The research will be concluded with a PhD thesis. A small teaching load (on average about 10%) is part of the job description.
The project will be supervised by Dr. Laura Genga and Dr.ir. Remco Dijkman. You will be based in the Information Systems group in the faculty of Industrial Engineering and Innovation Sciences. The PhD position is funded by EAISI (Eindhoven Institute of Artificial Intelligence Systems), so that collaboration will also involve AI researchers of the Institute in various disciplines.
Do you recognize yourself in this profile and would you like to know more? Please contact Laura Genga, l.genga[at]tue.nl. For information about terms of employment, please contact Susan Opgenoorth, HR Advisor, HRServices.IEIS[at]tue.nl or +31 40 2474465. Are you inspired to know more about working at TU/e? Please visit www.tue.nl/jobs.
Your application must contain the following documents (all in English):