Publication

Abstract : The developments in information and communication technologies pave the way to the digitization of traditional handwritten records to the computerized methods of record creation and storage. The healthcare industry is no different in adopting digitization mechanisms and the rate of adoption to the electronic health records (EHR) is rapid. With the introduction of many acts and regulations such as the Health Information Technology for Economic and Clinical Health Act introduced in 2009 and associated regulations such as Health information exchange act and False Claim Act, the rate has become exponential. It is evident that EHR hold beneficial knowledge for healthcare researchers and practitioners, and thus it is desirable to extract interesting patterns from the text. Clinical decision support systems heavily make use of information extracted from unstructured EHR for decision-making purposes. Clinical research studies also use such patterns extracted from unstructured EHR for analysis and predictions. Information extraction generally deals with automatic extraction of keyphrases, concepts, events, entities, and their relationships from unstructured text. This is a highly empirical area in natural language processing that uses lexical, syntactic, and semantic features for the extraction of useful information. As the volume and variety of the data increase, traditional rule-based information extraction systems will not perform well on such data and thus we need better algorithms that operate on text taking into consideration the meaning or semantics. A meaning-aware text processing algorithm can better extract patterns from highly unstructured text and can aid in the process of creating ontologies which are considered to be the backbone of semantic computing systems. Therefore it is evident that the research and development of technological breakthroughs in the areas of meaning-aware information extraction from healthcare documents and other electronic records is a highly demanding opportunity. This chapter is a novel attempt in this dimension that discusses a framework for building a meaning-aware healthcare information extraction from unstructured EHR. The proposed framework uses medical ontologies, medical catalog-based terminology extractor, and a semantic reasoner to build the medical knowledge base that is used for enabling a semantic information search and retrieval experience in the healthcare domain. An illustration of the same is also included in this chapter to better explain the proposed framework along with some future research dimensions on enhancing the framework discussed in this chapter. The empirical studies conducted show that the proposed framework outperforms some of the information extraction systems already available in the literature.