Resource management in the emergency department by using machine learning

Activity: Examination and supervisionSupervision of PhD students

Description

The project aims to develop decision support based on advanced statistical methods, machine learning (ML) and data from large health care registers, to optimize patient care in the emergency department.

The project is part of the AIR Lund (https://www.lupop.lu.se/airlund) which is a collaboration between Lund University, University of Halmstad, Region Skåne and Region Halland around register research using artificial intelligence (AI).

In Sweden, there is a unique opportunity to research various registers in health care. These registers have not been used to the extent possible with regard to the development of decision support based on statistical models or ML. Such decision support has enormous potential to improve diagnostics and treatment of patients seeking the emergency department. With refined and more individualized predictions, emergency department care could be better tailored to each patient. This in turn has the potential to increase patient safety and optimize resource utilization.
Period2021 Feb 1 → …
Examinee/Supervised personEllen Tolestam Heyman
Examination/Supervision held atEmergency medicine
Degree of RecognitionInternational

UKÄ subject classification

  • Clinical Medicine
  • Medical and Health Sciences

Keywords

  • Artificial intelligence
  • Emergency Medicine
  • Emergency Department
  • Ai
  • Machine learning