Healthc Inform Res.  2011 Dec;17(4):224-231. 10.4258/hir.2011.17.4.224.

A Multi-Classifier Based Guideline Sentence Classification System

• Song MH
• Kim SH
• Park DK
• Lee YH

• KMID: 2284549
• DOI: http://doi.org/10.4258/hir.2011.17.4.224

Abstract

OBJECTIVES
An efficient clinical process guideline (CPG) modeling service was designed that uses an enhanced intelligent search protocol. The need for a search system arises from the requirement for CPG models to be able to adapt to dynamic patient contexts, allowing them to be updated based on new evidence that arises from medical guidelines and papers.
METHODS
A sentence category classifier combined with the AdaBoost.M1 algorithm was used to evaluate the contribution of the CPG to the quality of the search mechanism. Three annotators each tagged 340 sentences hand-chosen from the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) clinical guideline. The three annotators then carried out cross-validations of the tagged corpus. A transformation function is also used that extracts a predefined set of structural feature vectors determined by analyzing the sentential instance in terms of the underlying syntactic structures and phrase-level co-occurrences that lie beneath the surface of the lexical generation event.
RESULTS
The additional sub-filtering using a combination of multi-classifiers was found to be more effective than a single conventional Term Frequency-Inverse Document Frequency (TF-IDF)-based search system in pinpointing the page containing or adjacent to the guideline information.
CONCLUSIONS
We found that transformation has the advantage of exploiting the structural and underlying features which go unseen by the bag-of-words (BOW) model. We also realized that integrating a sentential classifier with a TF-IDF-based search engine enhances the search process by maximizing the probability of the automatically presented relevant information required in the context generated by the guideline authoring environment.

Keyword

Knowledge Bases; Data Mining; Natural Language Processing

MeSH Terms

Data Mining
Humans
Hypertension
Imidazoles
Joints
Knowledge Bases
Natural Language Processing
Nitro Compounds
Search Engine
Imidazoles
Nitro Compounds

Figure

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  Figure 1 Searching clinical evidence enhanced with a sentential classifier. POS: part-of-speech.

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  Figure 2 A snapshot of the system user interface.

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  Figure 3 Parsed sentence from which to extract the feature vector. CC: coordinating conjunction, CD: cardinal number, IN: preposition or subordinating conjunction, JJS: adjective, superlative, MD: modal, NN: noun, singular or mass, NP: noun phrase, NNS: noun, plural, PP: prepositional phrase, QP: quantifier phrase, S: sentence, TO: to, VB: verb, base form, VBN: verb, past participle, VP: verb phrase.

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  Figure 4 Creating feature extractors.

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