  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.
