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Student for big data science project using deep learning in the emergency room

DepartmentStage, afstuderen en scriptie
Work locationGroningen
Apply no later than07 February 2022

Working environment

You start with a concise literature review and exploring the available data within Acutelines. Next, in collaboration with a technical physician, you will develop algorithms to pre-process complex data (i.e. photographs, electrophysiological waveforms) to identify features predictive of deterioration. Therefore, these features will be associated with deterioration using classical regression models, followed by integration into ML-models with demographic and medical data. In collaboration with the technical developer of Acutelines, you will work to further improve the database structure to collect high quality data from different sources, in accordance with legislation, regulations and to fit the needs of developing models to predict deterioration in sepsis. During monthly scientific group meetings, you will share your ideas and results with the research group. We offer the possibility to combine the project with a graduation research.

Job description

Sepsis is a dysregulated host response to an infection, which is associated with organ failure and can lead to death of the patient. The global burden of sepsis is high, as it affects 30 million people per year, leading to the death of 20% of these people. Recognition of early sepsis is critical to allow timely initiation of adequate treatment: antibiotics and supportive care. Clinical sepsis criteria to facilitate its diagnosis using a combination of vital parameters have a very limited sensitivity in the early phase and most physicians diagnose sepsis based on the clinical impression, also known as “gut feeling”. Importantly, the clinical impression of the physician is stronger associated with and better in predicting severity-of-illness than clinical sepsis criteria. The estimation by the physician is not only based on vital parameters such as body temperature, heart rate or blood pressure, but also takes the patient’s physical appearance into account and the pattern of parameters. Since rapid recognition of patients in need of medical care is critical among patients admitted to the emergency department (ED), we use big data to develop novel algorithms to improve early recognition of sepsis and identify which patients benefits the most from which therapy (personalized medicine) using deep learning. To facilitate this kind of research, we have set up the Acutelines data-biobank at the ED of the UMCG. The purpose of the Acutelines data-biobank is to improve prevention, recognition and treatment of acute conditions. A trained team of researchers screens all patients entering the ED, followed by data/biomaterial collection depending on broad selection criteria. In addition to demographic and medical data from the electronic patient file, we collect and store biomaterials (blood, urine, stool) for biomarker discovery, take a photograph of the face to predict deterioration using computer vision techniques and record continuous electrophysiological waveforms (i.e. ECG, PPG, EMG) to identify features predictive of deterioration. In the current project, we will identify novel risk markers predictive of deterioration in patients with sepsis at the ED and its interaction with therapy. Herefore, we make use of electrophysiological waveform analysis, photographs and the clinical impression of the health care professional that will be pre-processed prior to intergration into machine-learning models combined with demographic data and vital parameters.

To develop novel algorithms to identify patients who will benefit the most from specific therapy in sepsis (personalized medicine), we aim to:

  • Continuously improve the data warehouse structure to allow collection of high quality big data.
  • Develop algorithms to pre-process complex data (i.e. photographs, electrophysiological waveforms).
  • Integrate data in ML-models to identify risk markers predictive of deterioration in sepsis.
  • Identify risk markers predictive of efficacy of given therapy by integrating treatment data in the models.

What do we need

  • You are a student at the university (of applied sciences) with a focus on data science, such as computer science, data science (for life science), artificial intelligence. Life sciences students with a major on data sciences are also encouraged to apply.
  • You have good social and communication skills that you use correctly depending on the contact.
  • You are creative and have fresh ideas, also within set frameworks.
  • You have good writing skills.
  • You are ambitious and would like to learn new things.

What do we offer

  • Internship contract with the UMCG.
  • Good supervision in the UMCG.
  • Embedding within acute research (Acutelines, see www.acutelines.nl; Early sepsis research group, see www.sepsisonderzoek.nl)
  • Scientific working environment.
  • Possibility to conduct graduation research within the above project.
  • Fixed workplace in the office of Acutelines, equipped with a PC and telephone.
  • Nestor: an environment especially for graduates with information, links, tips & tricks that can help you during your graduation period at the UMCG.

More information

For more information about this vacancy you may contact:

Applying for a job

Please use the the digital application form at the bottom of this page - only these will be processed.
Within half an hour after sending the digital application form you will receive an email- confirmation with further information.
The application deadline stated in the vacancy overview does not apply to this vacancy. It will remain on the website until a suitable student has been found for the assignment.