The overwhelming flood of data in intensive care medicine precludes consistent judgement of medical interventions by humans. Therefore, computerized decision support is needed to assist the health care professional in making reproducible, high-quality decisions at the bedside. Traditional expert systems rely on a tedious, labor-intensive and time-consuming approach in their development which falls short of exploiting existing numerical and qualitative data in large medical databases. Therefore, we applied a new concept of combining time series analysis and a knowledge base system with learning and revision capabilities (MOBAL) for rapid development of decision support algorithms for hemodynamic management of the critically ill. This approach could be successfully implemented in an existing intensive care database handling time-oriented data to validate and refine the intervention rules. The generation of hypotheses for identified contradictions lead to conclusive medical explanations that helped to further refine the knowledge base. This approach will provide for a more efficient and timely development of decision support algorithms.