Automated generation of decision-tree models for the economic assessment of interventions for rare diseases using the RaDiOS ontology.

Objective: The development of decision models to assess interventions for rare diseases require huge efforts from research groups, especially regarding collecting and synthesizing the knowledge to parameterize the model. This article presents a method to reuse the knowledge collected in an ontology to automatically generate decision tree models for different contexts and interventions.

Material And Methods: We updated the reference ontology (RaDiOS) to include more knowledge required to generate a model.

Towards the automated economic assessment of newborn screening for rare diseases.

Objective: Economic assessments of newborn screening programs for rare diseases involve the use of models and require huge efforts to synthesize information from different sources. Sharing and automatically or semi-automatically reusing this information for new assessments would be desirable, but it is not possible nowadays due to the lack of suitable tools.

Material And Methods: We designed and implemented the Rare Diseases Ontology for Simulation (RaDiOS) after performing two reviews, and critically appraising the existing data repositories on rare diseases.