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Each of the four dimensions suggests the attributes' linkage to benefits and end-desirable beliefs.
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Attribute linkage applied a rule-based method to associate the identified attributes to the medication names [ 37].
The rule-based attribute linkage achieved the overall F-value of 99.1% (precision 98.7%, recall 99.4%, Table 3).
Since the attribute linkage process only linked the attributes to the identified medication names, it did not influence the recall of medication name detection (i.e. recall = 100%).
The results of attribute linkage suggested that even a relatively rudimentary, rule-based algorithm could yield high performance (overall F-value 99.1%, Table 3).
The output of the medication entity detector (Bullet A in Figure 2) was the input of the attribute linkage algorithm instead of the gold-standard annotations.
The hybrid algorithm achieved 95.0%/91.6%/93.3% of P/R/F on medication entity detection and 98.7%/99.4%/99.1% of P/R/F on attribute linkage.
To assess the attribute linkage process, the gold-standard medication and attribute annotations were fed into the algorithm and the performance was evaluated against the gold-standard medication-attribute associations.
In a double-annotated, gold-standard based evaluation of real-world medication reconciliation data, the proposed algorithm showed good capability in medication entity detection, attribute linkage and medication matching.
When combining the entity detection and the attribute linkage processes (processes 1 and 2 in Figure 2), the propagated F-value was 91.2% (precision 92.8%, recall 89.6%) across all attributes.> -wrap-foot> P indicates precision; R recall; F F-value. Table 4 shows the performances of the hybrid medication matching on gold-standard matched and discrepant medications respectively.
This approach models the integrated content of a product by characterizing its components and associated requirements with attributes and linkages.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com