Generating disease-pertinent treatment vocabularies from MEDLINE citations

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.

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

“…Biomedical relationship extraction is an important task in biomedical text mining. It has important applications in the fields of genedisease and protein-protein interactions, such as generated disease-pertinent treatment vocabulary from MEDLINE citations [13], chemical-induced disease relationship extraction from PubMed articles [11], and relationship extraction between proteins [14], [15].…”

Query Expansion Based on Wikipedia Word Embedding and BabelNet Method for Searching Arabic Documents

“…The user additional inputs are not relevant. A number of studies on health have been conducted by replacing the query with the Unified Medical Language Systems (UMLS) concept by calculating relevant documents that have the highest similarity using fuzzy [9], integrating MeSH (Medical Subject Headings) in tree form [10], retrieving information from MEDLINE vocabulary about information from various diseases, extracting and predicting based on semantic information, and turning information into UMLS concepts and predicting which treatment can be done [11]. Automatic query expansion is divided into three.…”

Automatic Relationship Verification in Online Medical Knowledge Base: a Large Scale Study in SemMedDB

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