A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication.

ABSTRACT: Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score's trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using th

SUBMITTER: Farzaneh N 

PROVIDER: S-EPMC8105342 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature