Abstract

Extracting the relations between medical concepts is very valuable in the medical domain. Scientists need to extract relevant information and semantic relations between medical concepts, including protein and protein, gene and protein, drug and drug, and drug and disease. These relations can be extracted from biomedical literature available on various databases. This study examines the extraction of semantic relations that can occur between diseases and drugs. Findings will help specialists make good decisions when administering a medication to a patient and will allow them to continuously be up to date in their field. The objective of this work is to identify different features related to drugs and diseases from medical texts by applying Natural Language Processing (NLP) techniques and UMLS ontology. The Support Vector Machine classifier uses these features to extract valuable semantic relationships among text entities. The contributing factor of this research is the combination of the strength of a suggested NLP technique, which takes advantage of UMLS ontology and enables the extraction of correct and adequate features (frequency features, lexical features, morphological features, syntactic features, and semantic features), and Support Vector Machines with polynomial kernel function. These features are manipulated to pinpoint the relations between drug and disease. The proposed approach was evaluated using a standard corpus extracted from MEDLINE. The finding considerably improves the performance and outperforms similar works, especially the f-score for the most important relation “cure,” which is equal to 98.19%. The accuracy percentage is better than those in all the existing works for all the relations.

Highlights

  • Biomedical information is abundantly available in journal articles and research studies in various databases, such as MEDLINE, PubMed, and Medscape

  • Mobile Information Systems aims to explore the extraction of drug-disease relation from biomedical texts. e paper proposes a semantic relation extraction approach between biomedical entities which exploits the specific features of these entities, which can be discovered by using a suggested Natural Language Processing (NLP) technique and Unified Medical Language System (UMLS) ontology. ese extracted features will form the input to the Support Vector Machine (SVM) classifier for the classification of relations between these entities

  • We used the standard corpus obtained from MEDLINE 2001. is corpus was annotated with types of semantic relationships between treatment (TREAT) and a disease (DIS). ese relationships were CURE, PREVENT, SIDE EFFECT, and NO CURE

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Summary

Introduction

Biomedical information is abundantly available in journal articles and research studies in various databases, such as MEDLINE, PubMed, and Medscape. Scientists need to automatically extract relevant information, for instance, semantic relations between medical entities, from these databases. Ese relations can be discovered from a variety of texts in biomedical literature. E relationship extraction studies have focused on specific types of relations, including interactions between protein and gene, protein and protein [6], drug and disease, and drug and drug [7]. Erefore, the objective of this study is to contribute to a better understanding of drug-disease relation. Mobile Information Systems aims to explore the extraction of drug-disease relation from biomedical texts. E paper proposes a semantic relation extraction approach between biomedical entities (drug and disease) which exploits the specific features of these entities, which can be discovered by using a suggested NLP technique and UMLS ontology. Various methods have been applied to extract relations from the biomedical literature [1,2,3,4,5]. e relationship extraction studies have focused on specific types of relations, including interactions between protein and gene, protein and protein [6], drug and disease, and drug and drug [7].

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