Liqin Wang1, Guilherme Del Fiol2, Bruce E Bray3, Peter J Haug4. 1. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Homer Warner Research Center, Intermountain Healthcare, 5121 South Cottonwood Street, Murray, UT 84107, USA. Electronic address: liqin.wang@utah.edu. 2. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA. 3. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Department of Internal Medicine, University of Utah, 30 North 1900 East, Salt Lake City, UT 84132, USA. 4. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA; Homer Warner Research Center, Intermountain Healthcare, 5121 South Cottonwood Street, Murray, UT 84107, USA.
Abstract
OBJECTIVE: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. METHODS: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. RESULTS: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. CONCLUSIONS: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.
OBJECTIVE: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. METHODS: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. RESULTS: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. CONCLUSIONS: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.
Authors: Qing T Zeng; Jonathan Crowell; Robert M Plovnick; Eunjung Kim; Long Ngo; Emily Dibble Journal: J Am Med Inform Assoc Date: 2005-10-12 Impact factor: 4.497
Authors: Liqin Wang; Suzanne V Blackley; Kimberly G Blumenthal; Sharmitha Yerneni; Foster R Goss; Ying-Chih Lo; Sonam N Shah; Carlos A Ortega; Zfania Tom Korach; Diane L Seger; Li Zhou Journal: J Am Med Inform Assoc Date: 2020-06-01 Impact factor: 4.497