WME 3.0: An Enhanced and Validated Lexicon of Medical Concepts (2018)

Abstract: Information extraction in the medical domain is laborious and time-consuming due to the insufficient number of domain-specific lexicons and lack of involvement of domain experts such as doctors and medical practitioners. Thus, in the present work, we are motivated to design a new lexicon, WME 3.0 (WordNet of Medical Events), which contains over 10,000 medical concepts along with their part of speech, gloss (descriptive explanations), polarity score, sentiment, similar sentiment words, category, affinity score and gravity score features. In addition, the manual annotators help to validate the overall as well as individual category level of medical concepts of WME 3.0 using Cohen’s Kappa agreement metric. The agreement score indicates almost correct identification of medical concepts and their assigned features in WME 3.0.

## introduction in the clinical domain, the representation of a lexical resource is treated as a crucial and contributory task because of handling several challenges. the challenges are the identification of medical concepts, their categories and relations, disambiguation of polarities, recognition of semantics whereas the scarcity of structured clinical texts doubles the challenges. in the last few years, several researchers were involved in developing various domain-specific lexicon such as medical wordnet and umls (unified medical language system) to cope up with such challenges. these lexicons help to bridge the gap between medical experts such as doctors or medical practitioners and non-experts such as patients @xcite . however, medical text is in general unstructured since doctors do not like to fill forms and prefer free-form notes of their observations. hence, a lexical design is difficult due to lack of any prior knowledge of medical terms and contexts. therefore, we are motivated to enhance a medical lexicon namely wordnet of medical events (wme 2.0) which helps to identify medical concepts and their features. in order to enrich this lexicon, we have employed various well-known resources like conventional wordnet, sentiword-net @xcite , senticnet @xcite , bing liu @xcite , and taboada's adjective list @xcite and a preprocessed english medical dictionary 1 on top of wme 1.0 and wme 2.0 lexicons @xcite . wme 1.0 contains 6415 number of medical concepts and their glosses, pos, polarity scores, and sentiment. thereafter, @xcite enhanced wme 1.0 by adding few more features as affinity score, gravity score, and ssw to the medical concepts and presented as wme 2.0. the affinity and gravity scores present the hidden link between the pair of medical concepts and the concept with the various source of glosses respectively. ssw of a medical concept refers the similar sentiment words (ssw) which follow the common sentiment property. in the current research, we have focused on enriching wme 2.0 with more number of medical concepts and including an additional feature i.e medical category. in order to develop such updated version of wme namely wme 3.0, we have taken the help of wme 1.0 and wme 2.0. we have also noticed that the previous versions of wmes are unable to extract knowledge-based information such as the category of the medical concepts and its coverage is also lower. therefore, we have enhanced the number of medical concepts as well as add category feature on top of wme 2.0. the current version, wme 3.0 contains 10,186 number of medical concepts and their category, pos, gloss, sentiment, polarity score, ssw, affinity and gravity scores. for example, wme 3.0 lexicon presents the properties of a medical concept say amnesia as of category (disease), pos (noun), gloss (loss of memory sometimes including the memory of personal identity due to brain injury, shock, fatigue, repression, or illness or sometimes induced by anesthesia.), sentiment (negative), polarity score (-0.375), ssw (memory loss, blackout, fugue, stupor), affinity score (0.429) and gravity score (0.170). moreover, to enhance and validate lexicon with the newly added medical concepts and categories, we have summarized our contributions as follows. the remainder of the paper is organized as follows: section 2 presents the related works for building a medical lexicon; section 3 and section 4 describe the previous versions of wmes like wme 1.0 and wme 2.0 and the development 2 http://alexabe.pbworks.com/f/dictionary+of+medical+ terms+4th+ed.-+(malestrom).pdf steps of wme 3.0; section 5 discusses the validation process of the proposed lexicon; finally, section 6 illustrates the concluding remarks and future scopes of the research. ## background biomedical information extraction is treated as one of the challenging research tasks as it deals with available medical corpora that are either unstructured or semi-structured. hence, a domainspecific lexicon becomes an essential component to convert a structured corpus from the unstructured corpus @xcite . also, it helps in extracting the subjective and conceptual information related to medical concepts from the corpus. various researchers have tried to build various ontologies and lexicons such as umls, snomed-ct (systematized nomenclature of medicine-clinical terms), mwn (medical wordnet), sentihealth, and wordnet of medical events (wme 1.0 and wme 2.0) etc. in the domain of healthcare @xcite @xcite . umls helps to enhance the access to biomedical literature by facilitating the development of computer systems that understand biomedical language @xcite . snomed-ct is a standardized, multilingual vocabulary that contains clinical terminologies and assists in exchanging the electronic healthcare information among physicians @xcite . furthermore, @xcite proposed medical wordnet (mwn) with two subnetworks e.g., medical factnet (mfn) and medical beliefnet (mbn) for justifying the consumer health. the mwn follows the formal architecture of the princeton wordnet @xcite . on the other hand, mfn aids in extracting and understanding the generic medical information for nonexpert groups whereas mbn identifies the fraction of the beliefs about the medical phenomena @xcite . their primary motivation was to develop a network for medical information retrieval system with visualization effect. senti-health lexicon was developed to identify the sentiment for the medical concepts @xcite . wme 1.0 and wme 2.0 lexicons were designed to extract the medical concepts and their related linguistic and sentiment features from the corpus @xcite . these mentioned ontologies and lexicons assist in identifying the medical concepts and their sentiments from the corpus but unable to provide the complete knowledge-based information of the concepts. hence, in the current work, we are motivated to design a full-fledged lexicon in healthcare which provides the linguistic, sentiment, and knowledge-based features together for the medical concepts. ## attempts for wordnet of medical events in healthcare, a domain-specific lexicon is required for identifying the conceptual and knowledge-based information such as category, gloss, semantics, and sentiment of the medical concepts from the clinical corpora @xcite . we have borrowed the knowledge from a domain-specific lexicon namely wordnet of medical events (wme) with its two different versions such as wme 1.0 and wme 2.0. these versions are distinguished according to the versatility and variety of medical concepts and their features. ## development of wme 3.0 a large number of daily produced medical corpora and their adaptable natures introduce the difficulty to build a full-fledged medical lexicon in healthcare domain. in order to resolve the issue, we have proposed a new version of wordnet of medical events namely wme 3.0. it is observed that wme 3.0 helps to extract more medical concepts and features from the unstructured corpus with respect to the previous version of wme, i.e., wme 2.0. another 3771 number of medical concepts and an additional category feature were newly added into wme 3.0. finally, wme 3.0 contains 10,186 medical concepts and their pos, categories, affinity scores, gravity scores, polarity scores, sentiments and ssw. to identify the additional medical concepts, we have employed the conventional wordnet foot_5 and medicinenet foot_6 resource. thereafter, we have written a script to extract new medical concepts, which are semantically (like common pos as well as sentiment) related with medical concepts of wme 2.0. besides, sentiword-net, senticnet, bing liu subjective list, taboada's adjective list, and previously mentioned preprocessed medical dictionary help to assign all features except category to 3771 medical concepts which were added. thereafter, we newly considered four different types of categories namely diseases, drugs, symptoms, and human anatomy for this research after examining the nature of medical concepts. in wme 3.0, all concepts are tagged with either the above-mentioned four categories or mmt category. mmt represents the miscellaneous medical terms which refer to the uncategorized and unrecognized medical concepts. in order to assign the category to the medical concepts, we have applied a well-known machine learning classifier, naïve bayes on top of wme 3.0 driven features. the classifier learns through the manually annotated 2000 medical concepts and their categories. thereafter, rest of 8186 medical concepts of wme 3.0 were processed by the classifier by predicting the category @xcite . for example, the medical concept ranitidine represents the category, drug in wme 3.0 lexicon. we have also noticed that the proposed wme 3.0 primarily contains pos as a noun, sentiment as negative, category as disease and drug, and n-gram feature as uni-grams and bi-grams. the observations could help to understand the characteristic of the lexicon and assist in designing various applications viz. medical annotation and concept network systems etc. the lexicon is very much demanding to identify four different types of categories and each medical concepts related gloss from a medical corpus, which presents the difference between wme 3.0 and already established very large scale semantic networks, such as umls. also, the lexicon-driven medical concepts and their features also assist in emulating human thought as a recommendation of medical advice, serving a potential foundation of a higher-order cognitive model under natural language processing @xcite . finally, the evaluation process of wme 3.0 as overall and its individual feature levels are discussed in the following section. ## evaluation in order to validate our proposed wme 3.0 lexicon, we have conducted the following result analysis. the result shows the agreement between two manual annotators to explain the acceptance of overall lexicon as well as its individual features. the agreement has been calculated using cohen's kappa coefficient score κ which is defined in equation 1 @xcite . ## conclusion and future work the present task has been motivated to enrich a medical lexicon with additional medical concepts and a feature called category in wme 3.0. in order to prepare the current version, we have employed previous two versions of wme viz. wme 1.0 and wme 2.0 along with various well-defined lexicons and a machine learning classifier. wme 3.0 contains 10,186 medical concepts and eight different types of useful features such as category and gloss etc. in addition, we have also validated wme 3.0 from two different aspects, namely overall evaluation and usefulness of individual feature with the help of two manual annotators. the annotators provided agreement scores that were processed using cohen's kappa agreement analysis. finally, the κ scores showed the importance of wme 3.0 in healthcare. in future, we will attempt to enhance wme 3.0 with more number of medical concepts as well as syntactic and semantic features for improving the coverage and quality.