Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders (AID-Spine, part I)

We will use machine learning methods on large survey and health register data in order to identify people with different treatment trajectories and health outcomes after surgical and/or conservative treatment for spinal disorders.

Aims and objectives

Prediction models for health outcomes will be validated and further developed to clinical decision-support tools, which can help patients and clinicians to make better decisions and ensure more personalized treatments.

The overarching aim of the AID-Spine project is to address health and welfare challenges in spinal disorders by aiming for a future personalized and sustainable healthcare. There is lack of feasible and precise clinical prognostic tools which can help patients and clinicians to make better decisions and ensure more personalized treatments.

The primary objective of AID-Spine part I 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 1) conduct external validation of the prediction models in data sets from Denmark and Sweden, and 2) explore how the prediction models can be implemented into AI-based clinical co-decision tools and interventions.

Members

AID-Spine project members cover a broad interdisciplinary group, including neurosurgeons, physical therapists, data scientists, epidemiologists, statisticians, a user panel, and clinicians working with spinal disorders. The AID-Spine project has two PhD and one Postdoc candidate.

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    The AID-Spine project is part of the Centre for Intelligent Musculoskeletal health, which is one out of five excellent academic environments at OsloMet.