Three-stage intelligent support of clinical decision making for higher trust, validity, and explainability
This work addresses the challenge of making data-driven CDSSs more trustworthy and scalable for real-world clinical applications, though it appears incremental by building on existing scales and interfaces.
The paper tackled the problem of low applicability and scalability of clinical decision support systems (CDSSs) by proposing a three-stage approach that integrates regulatory policy, data-driven modes, and interpretation procedures, resulting in improved clinical scales like FINDRISK for T2DM prediction while maintaining compatibility and trust.
The paper presents an approach for building consistent and applicable clinical decision support systems (CDSSs) using a data-driven predictive model aimed at resolving the problem of low applicability and scalability of CDSSs in real-world applications. The approach is based on a threestage application of domain-specific and data-driven supportive procedures that are to be integrated into clinical business processes with higher trust and explainability of the prediction results and recommendations. Within the considered three stages, the regulatory policy, data-driven modes, and interpretation procedures are integrated to enable natural domain-specific interaction with decisionmakers with sequential narrowing of the intelligent decision support focus. The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSSs. The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling us to improve known clinical scales (such as FINDRISK) while keeping the problem-specific reasoning interface similar to existing applications. Such inheritance, together with the three-staged approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solutions in real-world cases.