PhD student Public Health in the new ICAI Stroke Lab: from 112 to Rehabilitation

Each year, more than 30,000 patients with ischemic stroke are admitted in a Dutch hospital and this number is increasing. Endovascular thrombectomy is a new treatment that was introduced in 2015 and strongly improves functional outcomes of stroke patients. However, major challenges remain. One of the most prominent is that despite a successful endovascular thrombectomy one to two thirds of patients still have a poor outcome. Moreover, further down the clinical management of stroke patients, identifying those individuals who benefit most from rehabilitation and providing patients with realistic outcome estimations are also of great importance. In this PhD project you will contribute to solving these challenges. You will analyse large clinical and radiolodical data sets to optimize endovascular therapy and rehabilitation trajectories based on pre- peri- and post-interventional parameters.

The PhD project is one of the five PhD positions in the ICAI Stroke Lab, one of the ROBUST program's labs. The ROBUST program on Trustworthy AI-based Systems adds 17 new labs to the ICAI's current ecosystem. The aim of the ICAI Stroke lab is to improve the outcome of stroke patients by optimizing the whole chain from the 112-call to rehabilitation with AI. Specifically, we intend to develop and assess AI-based patient stratification (faster, more accurate) for acute stroke care interventions, AI based functional outcome prediction after endovascular therapy, image-guidance in endovascular interventions for better therapeutic outcome and data-driven individualized stroke recovery policies, based on patients' current abilities and on constantly updated prognostic models for stroke recovery. In addition, we will study the acceptance of such AI-models by clinical decision-makers. There will be strong collaboration between the PhD students in the ICAI lab.

We are looking for highly motivated candidates with a university MSc degree, who want to contribute to the rapidly developing field of stroke treatment. Candidates are expected to have outstanding research skills, to be well organized and result-driven, and to have a strong team spirit

• The candidate should have a clinical sciences degree (e.g., Medicine, Epidemiology, Clinical Health Sciences, (Technical) Medicine, Human Movement Sciences) with an interest in data science and machine learning. Or an engineering degree (e.g., Biomedical Engineering, Computer Science, Data Science, Applied Mathematics, or related field) with an interest in medical applications.
• Candidates also must have strong English language skills (written and oral).

The research in the ICAI Stroke Lab is a collaborative effort by the Departments of Public Health, Neurology, Epidemiology, Rehabilitation Medicine, and Radiology & Nuclear Medicine from the Erasmus MC, the Erasmus School of Health Policy & Management from the Erasmus University, and Philips Healthcare.

For this position you will be primarly based in the department of Public Health, but also be affiliated to the departments of Neurology and Radiology & Nuclear Medicine. The research will be conducted under the supervision of prof. dr. Hester Lingsma (Public Health), dr. Bob Roozenbeek (Neurology), dr. Daniel Bos (Radiology & Nuclear Medicine / Epidemiology) and dr. Theo van Walsum (Radiology & Nuclear Medicine).

At the Department of Public Health, we are dedicated to improve population health at local, national, and international level through excellent research and education. Our research is characterised as highly multidisciplinary and is focused on the development and use of innovative methods. Collaboration with and direct impact on practice and policy are at the heart of our work. The reseach line Medical Decision Making, led by prof. dr. Hester Lingsma aims to support patients, clinicians and healthcare policymakers in making the best decisions about diagnostic, therapeutic and other medical interventions. Our mission is to contribute to optimal evidence-based and personalised decisions in healthcare through outstanding quantitative research, in close collaboration with the clinic.