The introduction of electronic medical records (EMRs) enabled the access of unprecedented volumes of clinical data, both in structured and unstructured formats. A significant amount of this clinical data is expressed within the narrative portion of the EMRs, requiring natural language processing techniques to unlock the medical knowledge referred to by physicians. This knowledge, derived from the practice of medical care, complements medical knowledge already encoded in various structured biomedical ontologies. Moreover, the clinical knowledge derived from EMRs also exhibits relational information between medical concepts, derived from the cohesion property of clinical text, which is an attractive attribute that is currently missing from the vast biomedical knowledge bases. In this paper, we describe an automatic method of generating a graph of clinically related medical concepts by considering the belief values associated with those concepts. The belief value is an expression of the clinician's assertion that the concept is qualified as present, absent, suggested, hypothetical, ongoing, etc. Because the method detailed in this paper takes into account the hedging used by physicians when authoring EMRs, the resulting graph encodes qualified medical knowledge wherein each medical concept has an associated assertion (or belief value) and such qualified medical concepts are spanned by relations of different strengths, derived from the clinical contexts in which concepts are used. In this paper, we discuss the construction of a qualified medical knowledge graph (QMKG) and treat it as a BigData problem addressed by using MapReduce for deriving the weighted edges of the graph. To be able to assess the value of the QMKG, we demonstrate its usage for retrieving patient cohorts by enabling query expansion that produces greatly enhanced results against state-of-the-art methods.