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Clinical prediction models in critical illness: from computer to bedside

Employer: KU Leuven
Posted: June 18, 2014
Expires: August 17, 2014
Requisition number: DOCO-2014-62

Science jobs from KU Leuven:
Place : Leuven Apply no later than : August 31, 2014

Intensive Care Medicine Research Group of the University of Leuven, Belgium, is looking for a highly motivated candidate with strong interests in application-driven research in the fields of machine learning and intensive care medicine.

Clinical prediction models in critical illness: from computer to bedside.

Attention! Financing for this position is not guaranteed, but will need to be applied for with the support of a supervisor at a scholarship organization. Applying to this position will bring you in contact with a potential supervisor. The internationally renowned Intensive Care Medicine Research Group, led by Prof. Dr. Greet Van den Berghe, is embedded in the department of Cellular and Molecular Medicine at the University of Leuven (Belgium). Our group combines a research laboratory with a large ICU (65 beds, over 2500 critically ill patients each year), both located on the Health Sciences Campus of Gasthuisberg. This unique setting allows a very fast and effective interaction among clinicians and basic researchers within the research team, allowing research from 'bed to bench and from bench to bed'. Our overall research objective is to unravel key pathways underlying critical illness-induced organ failure, thereby identifying potential therapeutic targets to enhance recovery. We focus on endocrine and metabolic underlying mechanisms of organ-specific problems evoked by critical illness. One of our research interests is the potential for early detection of illness by using advanced computational techniques, to allow for an early and optimal administration of treatments.

Website unit:

h1. Project

Critical Care Medicine is a relatively young branch in modern medicine.The first ICU's started in the 1950's, but it wasn't until the 1970's and 1980's that ICU's have boomed worldwide and the discipline quickly became a high-tech branch of medicine that combines clinical skills, powerful drugs, and sophisticated mechanical devices to support the function of vital organs. This allows patients to survive a variety of previously lethal insults such as multiple trauma, extensive surgery or severe infections. Intensive care is sometimes referred to as 'the art of managing extreme complexity'. Despite this dedicated care, mortality among critically ill patients who require intensive care for more than a few days remains around 20% worldwide. Critical illness affects millions of patients each year worldwide, and consumes a large fraction of health care resources. It is therefore of great interest to detect those patients most vulnerable to specific organ deteriorationas early as possible, in order to administer dedicated therapies earlier and hopefully prevent the chronic and lethal phases of critical illness.

The typical ICU generates vast amounts of data from several monitoring systems for each patient. At the department of Intensive Care Medicine of the university hospitals Leuven, this data is electronically collected as time series of varying resolutions and integrated in a patient data management system (PDMS). Using data mining and machine learning techniques we have previously developed clinically relevant prediction models from the PDMS data, that outperform commonly used risk scores and that perform on par with experienced physicians. Our department is involved in national and international collaborations to share data with other large ICU's. We strongly believe the time is right to transition early detection models into intelligent warning systems to be used for decision support by the physician, on the daily evaluation of the individual ICU patients. Knowledge generation from 'big data' is an important research path in modern medical science. Our research group is at the forefront of this trend.

With the help of the interdisciplinary research team, where clinicians, basic biomedical scientists, and computer scientists work in close collaboration, the PhD student will gradually be able to acquire the knowledge and skills to perform in-depth analysis of clinical and research databases collected at our ICU or gathered in international collaboration with other ICU's, to develop novel models for early detection of organ-specific critical illness. The ultimate goal of this project is to translate these models into bedside tools to be used at the bedside of the ICU patient, and validate them in a prospective randomized clinical trial.

h2. Profile

The candidate should have a strong academic record and a Masters diploma in the fields of Bio-informatics, Computer Science or Engineering. Previous research experience is a plus, but not essential. The candidate should be willing to think out of the box, and be motivated to learn and understand aspects of the field of application. Knowledge of data analysis, statistics, machine learning and good programming skills are essential. Knowledge of R, Matlab, Python and/or Weka is an advantage. Since we are an international team, English is the main language and proficiency in written and spoken English is therefore crucial. The selected candidate is expected to write a doctoral thesis on her/his research after 4 years.

h2. Offer

Fulltime 4-years PhD position in an international research team at the KU Leuven. The KU Leuven is one of Europe's leading research universities. The Intensive Care Medicine Research Group offers a dynamic and intellectually challenging environment, in close collaboration with experts from a wide variety of domains. A thorough scientific education, the possibility to become a world-class researcher. The possibility to participate in international conferences and collaborations.

h2. Interested?

For more information please contact Prof. dr. Geert Meyfroidt, tel.: +3216348746, mail: or Mr. Fabian Guiza Grandas, tel.: +32 16 37 91 53, mail:

You can apply for this job no later than August 31, 2014 via the online application tool

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