We will use machine learning methods on large surveys and health registers in order to develop and validate prediction models for health and welfare outcomes after surgical or conservative treatment for spinal disorders.
Wiser choices of treatment for spinal disorders are necessary to make healthcare more efficient and sustainable. There is lack of feasible and precise clinical decision aids, which can help patients and clinicians to make better decisions and ensure more personalized treatments.
Prediction models for health and welfare outcomes will be developed and validated by advanced Machine Learning methods. The best models will be implemented in clinical decision aids and further tested with respect to feasibility.
The project covers a broad interdisciplinary group, including neurosurgeons, physical therapists, data scientists, epidemiologists, statisticians, a user panel, and clinicians working with spinal disorders.
-
Participants
Loading ... -
More about the project
The overarching aim of the AIS-Spine project is to address health and welfare challenges in spinal disorders by aiming for a future personalized and sustainable healthcare.
The primary objective of the AID-Spine part I project is to use machine learning methods on large survey and health register data to identify people with different treatment trajectories and health outcomes after surgical and/or conservative treatment for spinal disorders.
Secondary objectives are to
- conduct external validation of the prediction models in data sets from Denmark and Sweden
- explore how the prediction models can be implemented into AI-based clinical co-decision tools and interventions
Organisation
This project is organized in three work packages (WPs). The primary objective will be investigated in WP1, and the secondary objectives will be investigated in WP2 and WP3, respectively.
In WP1 we will use data from general population surveys (HUNT, Tromsø, and Ullensaker surveys) and administrative health registry data (Norwegian Patient Registry, NPR, and Norwegian Registry for Primary Health Care, NRPHC).
In WP2 the risk and prognostic models will be validated in Danish and Swedish data sets (SpineData, Danish Spine Database (DaRD), DaneSpine and SweSpine).
In WP3 the validated risk and prognostic models will be integrated in clinical decision-making tools and tested in different clinical settings, e.g. in the first consultation between surgeon and patient referred for surgical assessment.
The feasibility of implementing the clinical decision-making tools will be explored by qualitative interviews and observation in participatory co-design approach.
