Search is usually based on syntactical filtering and TF/IDF relevance ranking. This may not meet a clinician's expectations for patients with complex medical needs. The searchable knowledge space contains both invariant (not specific to patients) and variant (specific to patients) data. Our solution leverages a growing medical knowledge base for the choosing what to present to benefit the patient. Typical question: What is the best treatment for my 87 year old male obese patient complaining of XXX with High blood pressure, diabetes mellitus 2, taking a long list of meds and herbals? To address this a semantical (knowledge graph based is leveraged) and there may be possible interactions between the various conditions/diseases/meds/herbals. Clinicians mentally weigh the risk/benefit for each patient they encounter. To accurately diagnose or treat a patient requires accounting for multiple factors including the patient's demographic info, age, fragility, treatment pathways for each condition/disease, past medical history, comorbidities, current medications, social history and family history (including genetics). We leverage the Clinical Reasoning Engine (CRE) as our "non-linear" reasoning symbolic AI engine executing complex clinical logic over an ever changing/growing knowledge graph of data from multiple data sources and from multiple contexts. An effective clinician accomplishes this task by using a combination of experience, skill, and data. With medical data and knowledge doubling approximately every 73 days, the CRE is proving to be a valuable aide.
