Abstract
This paper proposes an approach to leverage upon existing ontologies in order to automate the annotation of time series medical data. The annotation is achieved by an abductive reasoner using parsimonious covering theorem in order to determine the best explanation or annotation for specific user defined events in the data. The novelty of this approach resides in part by the system’s flexibility in how events are defined by users and later detected by the system. This is achieved via the use of different ontologies which find relations between medical, lexical and numerical concepts. A second contribution resides in the application of an abductive reasoner which uses the online and existing ontologies to provide annotations. The proposed method is evaluated on datasets collected from ICU patients and the generated annotations are compared against those given by medical experts.
Highlights
Medical monitoring of patients is becoming increasingly device supported and large volumes of high frequency data are generated from sensors that monitor physiological parameters
The first dataset contains 12-hours of time-series data from a set of medical sensors measuring heart rate, arterial pressure, and arterial oxygen saturation of an infant in an Intensive Caring Unit (ICU). This patient is suffering from several diseases, namely “multiple liver abscesses”, “portal hypertension” and “E. Coli sepsis”, used as the ground truth for the evaluation of the final explanations suggested by the reasoner
This package of data is the ICU data package provided for use in 1994 AI in Medicine symposium submissions [10]
Summary
Medical monitoring of patients is becoming increasingly device supported and large volumes of high frequency data are generated from sensors that monitor physiological parameters. While the use of such technologies enables a continuous monitoring, the complexity and amount of data creates a challenge for the medical staff to provide interpretations. Linked data which refers to a set of structured data, namely global data space, has become a paradigm providing the transition from document oriented Web into a web of interlinked data [21]. According to this paradigm, unstructured information represented in web pages is mapped into the RDF graph which is understood as a set of subject-predicate-object triples, T = (S, P, O) [4]. An answer for this query is achieved by binding variables of the query triples into (U ∪ L)
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