We study the expressive power of independence-friendly quantifier prefixes composed of universal$\left( {\forall x/X} \right)$, existential$\left( {\exists x/X} \right)$, and majority quantifiers$\left( {Mx/X} \right)$. We provide four quantifier prefixes that can express NP hard properties and show that all quantifier prefixes capable of expressing NP-hard properties embed at least one of these four quantifier prefixes. As for the quantifier prefixes that do not embed any of these four quantifier prefixes, we show that they are equivalent to a first-order quantifier prefix composed of$\forall x$,$\exists x$, and Mx. In unison, our results imply a dichotomy result: every independence-friendly quantifier prefix is either decidable in LOGSPACE or NP hard.
In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.
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