Knowledge-Based Interpolation of Time-Oriented Clinical Data

Temporal interpolation is the task of bridging gaps between time-oriented clinical data or abstracted concepts in a context-sensitive manner. It is one of the subtasks important for solving the temporal-abstraction task—abstraction of interval-based, higher-level concepts from timestamped clinical data. We present a knowledgebased approach to the temporal-interpolation task. The temporal-interpolation mechanism we discuss relies, among other knowledge types, on a temporal-persistence model. This model employs local temporal-persistence functions that are temporally bidirectional (i.e., extend a belief measure in a predicate both into the future and into the past) and global, maximal-gap temporalpersistence functions that bridge gaps between interval-based predicates. We investigate the quantitative and qualitative properties implied by both types of persistence functions. Our goal is to formulate the knowledge required for solving the temporal-abstraction task, and in particular the temporal-interpolation subtask, so as to facilitate the acquisition of that knowledge, its maintenance, its reuse for the same task in different domains, and its sharing among different applications in the same domain. We have implemented our approach and evaluated it in several clinical domains. 1 Temporal-Abstraction and Temporal Interpolation Time-stamped clinical data often need to be abstracted in a context-sensitive manner into more abstract, interval–based concepts, meaningful for a specific medical domain (e.g., oncology) and a particular task (e.g., monitoring of patients that are being treated by chemotherapy). We term this interpretation task the temporal-abstraction (TA) task. For instance, most clinical tasks require measurement and capture of numerous patient data. An automated, knowledge-based decision-support tool that assists physicians should provide short, informative, contextsensitive summaries, at various desirable levels of abstraction, of time-oriented clinical data stored on electronic media. Data abstraction assists both physicians and automated decision-support systems. A meaningful summary characterizes significant features over periods of time, such as "2 weeks of grade-II bone-marrow toxicity in the context of therapy for potential complications of a bonemarrow transplantation event” (Figure 1). Solving the TA task involves the solution of several subtasks (see Section 2). One of these tasks is the temporal-interpolation task: bridging gaps between pointor interval-based temporal predicates of a similar-type that are temporally disjoint, to create longer intervals (see Figure 1). Temporal interpolation requires, among other knowledge types, some measure of temporal persistence of temporal predicates (denoting either raw data or abstract concepts). For instance, if we measured hemoglobin levels on Tuesday and on Friday, both being abstracted as LOW, was the patient’s hemoglobin level on Thursday also LOW? ” In fact, the very notion of an episode implies some form of bounded persistence of concepts over time, preventing the clumping together of similar, but distinct instances of the same concept. The concept of persistence was addressed previously; we discuss the relationship of such work to ours in Section 6. 2 Knowledge-Based Temporal Abstraction The framework we employ for solving the TA task is the knowledge-based temporal-abstraction (K B T A ) method [Shahar, 1997]. The KBTA method is a general problem-solving method [Eriksson et al., 1995] for interpreting data in time-oriented domains, with clear semantics for both the method and its domain-specific knowledge requirements. The KBTA method comprises a knowledge-level representation of the TA task and of the knowledge required to solve that task. The KBTA method has a formal model of input and output entities, theirrelations, and properties associated with these entities— the KBTA ontology. The KBTA method decomposes the TA task into five parallel subtasks :