Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Mining knowledge from text using information extraction
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
Approaches to text mining for clinical medical records
Proceedings of the 2006 ACM symposium on Applied computing
A Generic Framework: From Clinical Notes to Electronic Medical Records
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Journal of Biomedical Informatics
A survey and classification of semantic search approaches
International Journal of Metadata, Semantics and Ontologies
Distinguishing historical from current problems in clinical reports: which textual features help?
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Concept-graph based biomedical automatic summarization using ontologies
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
Summarization from medical documents: a survey
Artificial Intelligence in Medicine
Bio-medical entity extraction using support vector machines
Artificial Intelligence in Medicine
Semantic annotation for knowledge management: Requirements and a survey of the state of the art
Web Semantics: Science, Services and Agents on the World Wide Web
MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Relation-Based document retrieval for biomedical literature databases
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
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Currently health care industry is undergoing a huge expansion in different aspects. Advances in Clinical Informatics (CI) are an important part of this expansion process. One of the goals of CI is to apply Information Technology for better patient care service provision through two major applications namely electronic health care data management and information extraction from medical documents. In this paper we focus on the second application. For better management and fruitful use of information, it is necessary to contextually segregate important/ relevant information buried in a huge corpus of unstructured texts. Hence Information Extraction (IE) from unstructured texts becomes a key technology in CI that deals with different sub-topics like extraction of biomedical entity and relations, passage/paragraph level information extraction, ontological study of diseases and treatments, summarization and topic identification etc. Though literature is promising for different IE tasks for individual topics, availability of an integrated approach for contextually relevant IE from medical documents is not apparent enough. To this end, we propose a compositional approach using integration of contextually (domain specific) constructed IE modules to improve knowledge support for patient care activity. The input to this composite system is free format medical case reports containing stage wise information corresponding to the evolution path of a patient care activity. The output is a compilation of various types of extracted information organized under different tags like past medical history, sign/symptoms, test and test results, diseases, treatment and follow up. The outcome is aimed to help the health care professionals in exploring a large corpus of medical case-studies and selecting only relevant component level information according to need/interest.