For clinical decision support systems, the disease knowledge base accounts for the majority of the total development cost, because it requires considerable effort by highly-paid domain experts. However, existing research on automated acquisition of medical knowledge has focused on accuracy, while mostly ignoring the cost issue. This case study investigates the cost breakdown of our simplified disease knowledge base, and discusses a way of reducing development cost of the core component for clinical decision support systems.
To achieve broad disease coverage with limited cost, we adopted a hybridization approach, that is, combining a handmade knowledge base for essential diseases and an automatically generated knowledge base for rare diseases. Toward further cost reduction, the case study suggested to minimize the human intervention by medical experts through i) keeping the structure of the knowledge base simple, ii) establishing a public resource for laboratory examination results, and iii) lazy evaluation of data quality in the utilization phase. Although the resulting knowledge base may not be adequate for a definitive diagnosis, the approach could be suitable to build clinical decision support systems for differential diagnosis and for disease search engines.