Accession Number : ADA618948


Title :   KISTI at TREC 2014 Clinical Decision Support Track: Concept-based Document Re-ranking to Biomedical Information Retrieval


Descriptive Note : Conference paper


Corporate Author : KOREA INST OF SCIENCE AND TECHNOLOGY INFORMATION DAEJEON (KOREA)


Personal Author(s) : Oh, Heung-Seon ; Jung, Yuchul


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a618948.pdf


Report Date : Nov 2014


Pagination or Media Count : 11


Abstract : With fast development of medical information systems and software, clinical decision support (CDS) systems continue to develop new methods to deal with diverse information coming from heterogeneous sources such as a large volume of electronic medical records (EMRs), patient genomic data, existing genomic pharmaceutical databases, curated disease-specific databases, peer-reviewed research, etc. As an avenue towards advanced clinical decision-making, TREC CDS track focuses on developing new techniques to find medical cases that are useful for patient care from biomedical literature. Meanwhile, given the volume of the existing literature, and the diversity in biomedical field, finding & delivering relevant medical cases for a particular clinical need is a non-trivial task. Moreover, understanding three kinds of different topics (i.e. diagnosis, test, and treatment) and retrieving appropriate biomedical research articles are quite challenging. To address these problems, we propose concept-based document re-ranking approaches to clinical documents. We basically use pseudo relevance feedback for query expansion to retrieve initial relevant documents. In addition, we considered two different concept-based re-ranking approaches which utilize popular external biomedical knowledge resources (i.e. Wikipedia and UMLS) for improving biomedical information retrieval. Our concept-based re-ranking approaches are to bridge the gaps between queries and biomedical research articles in semantic level.


Descriptors :   *BIOMEDICAL INFORMATION SYSTEMS , *COMPUTER AIDED DIAGNOSIS , *INFORMATION RETRIEVAL , CLASSIFICATION , CLINICAL MEDICINE , DATA MINING , DECISION SUPPORT SYSTEMS , HEALTH CARE FACILITIES , HETEROGENEITY , KNOWLEDGE MANAGEMENT , MAPPING , MAXIMUM LIKELIHOOD ESTIMATION , MEDICAL RESEARCH , ONTOLOGY , PATIENTS , PATTERN RECOGNITION , PROBABILITY DISTRIBUTION FUNCTIONS , QUALITATIVE ANALYSIS , RANK ORDER STATISTICS , RECORDS , SEMANTICS , TEST AND EVALUATION , TEXT PROCESSING


Subject Categories : Information Science
      Medicine and Medical Research
      Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE