Chemical named entities recognition: a review on approaches and applications

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracte

Pattern-based system to detect the adverse drug effect sentences in medical case reports

Background The detection of adverse drug effect sentences in medical text reduces the efforts required for the manual task of drug safety monitoring by decreasing the number of reports which need to be investigated by drug safety experts. Moreover it helps in compiling a highly accurate and machine-understandable drug-related adverse effects knowledge bases which can support pharmacovigilance research and aids computational approaches for drugs repurposing. In this study, we proposed a pattern-based method to detect the sentences containing drug-adverse effect causal relation from medical case reports. Materials and methods A set of 500 full abstracts from Medline medical case reports containing 988 adverse drug effect sentences from ADE corpus were used to evaluate the sentences detection task. Our method combined an outcome of a concept recognition system with a method for automatic generation of numerous patterns. Results Our method achieved recall of 72.8, precision of 93.6 and F-Score of 81.7 % in the adverse drug effect sentences detection task. Conclusion The results of this study can help database curators in compiling medical databases and researchers to digest the huge amount of textual information which is growing rapidly. Moreover, the mining algorithms developed in this study can be employed to detect sentences contain new associations between other medical entities in medical text

Mining lexico-syntactic patterns to extract chemical entities with their associated properties

Specific information on newly discovered compound is often difficult to be found in chemical databases. The chemical and drug literature is very rich with the information resulted from new chemical synthesis. This paper presents a survey on the types of approaches that have been used to extract information associated with chemical compounds from chemical and drug text. Thereafter, it gives a description for a novel pattern-based extraction method to be developed in the future taking into account specific types of information associated with chemical compounds not explored before in the automated extraction from a text. The paper focuses on the extraction of the properties that influence the bioavailability of drug candidates' compounds. The result of this study can help the database curators in compiling the drug related chemical databases and the researchers to digest the huge amount of textual information which is growing rapidly

Pattern-based system to extract and distinguish drugadverse effect relation from other drug-medical condition relations in the same sentence

Extraction of drug-adverse effect causal relationship supports pharmacovigilance research and reduces the manual efforts for some tasks such as drug safety monitoring and building databases for adverse drugs effects from free text. In this study, we proposed a pattern-based method to extract drug-adverse effects causal relation from medical case reports and to distinguish this relation from other drug-medical condition relations exist in the same sentences. For training and evaluation purposes; we used 481 sentences from ADE corpus. Our method combined a concept recognition system with a module for drug-adverse effect relation extraction and discrimination task based on automatic generated numerous patterns and the position of matched pattern in a sentence. Our method achieved recall of 36.1, precision of 30.6 and an F-Score of 33.1 .The result of this study provides rapid extraction of machine-understandable drug-adverse effects pairs which can help in many computational drug researches