Data Mining Infrastructure to Characterize Medically Unexplained Symptoms

The field of medically unexplained symptoms (MUS) in post-deployment situations presents a unique opportunity for enhancing further understanding and prospective monitoring. Silico Insights structured its Phase I efforts in two areas: literature review, and assessment of critical data elements for reliable clinical analysis. In general, the availability of electronic information and infrastructure today is heartening for a proposed Phase II development of a MUS-based data mining infrastructure. Specifically, existing clinical cohort-type analyses of post-deployment responses provide insight on what data may provide most information, what analyses may extract the most features and how a proactive approach may be implemented for MUS. In implementing steps towards a productive completion of a Phase II effort, the following key questions will be addressed: 1. What data hold the most information relevant to MUS? 2. What data elements among these data are not currently collected reliably? 3. What are the minimum "new" data elements most critical for future MUS analyses? 4. How can these critical elements be reliably collected and combined with other required existing data fields to provide the basis for MUS data mining? 5. What analytics would be needed for extracting the most value from such a database?