Before chief complaints can be analyzed by computerized biosurveillance systems, they must be converted from English (or other natural language) into computer-interpretable format. Biosurveillance systems typically use natural language processing (NLP) to convert chief complaints into computer-interpretable format. We are aware of one system that takes advantage of a routine translation of chief complaints into computer-interpretable form (Beitel et al., 2004).
There are two basic NLP methods for converting free-text chief complaints into computer-interpretable format—keyword parsing and probabilistic. We discussed these methods in Chapter 17 and will not repeat the discussion here.
The NLP component of a biosurveillance system analyzes a recorded chief complaint to classify a patient into a syndrome category. Some biosurveillance systems use NLP to identify syndromes directly and others use NLP to identify symptoms in the chief complaint and subsequently use Boolean (AND, OR, NOT) or probabilistic combinations of symptoms to assign a syndrome.
The subsequent (non-NLP) analysis performed by a biosurveillance system searches for clusters of syndromes in space, time, and/or demographic strata of a population, as discussed in Part III.
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