Context-Sensitive Temporal Abstraction of Clinical Data

Temporal-abstraction in medical domains is the task of abstracting higher-level, interval-based concepts (e.g., 3 weeks of moderate anemia) from time-stamped clinical data (e.g., daily measurements of hemoglobin) in a context-sensitive manner. We have developed and implemented a formal knowledge-based framework for decomposing and solving that task that supports acquisition, maintenance, reuse of domain-independent temporal-abstraction knowledge in different clinical domains, and sharing of domain-specific temporal-abstraction properties among different applications in the same domain. In this chapter, we focus on the representation necessary for creation during runtime of appropriate contexts for interpretation of clinical data. Clinical interpretation contexts are temporally extended states of affairs (e.g., effect of insulin as part of the management of diabetes) that affect the interpretation of clinical data. Interpretation contexts are induced by measured patient data, concluded abstractions, external interventions such as therapy administration, and the goals of the interpretation process. We define four types of interpretation-contexts (basic, composite, generalized, and nonconvex), discuss the conceptual and computational advantages of separating interpretation contexts from both the propositions inducing them and the abstractions created within them, and provide an example within the domain of monitoring diabetes patients.