Exploiting the UMLS Metathesaurus for extracting and categorizing concepts representing signs and symptoms to anatomically related organ systems

AbstractObjectiveTo develop a method to exploit the UMLS Metathesaurus for extracting and categorizing concepts found in clinical text representing signs and symptoms to anatomically related organ systems. The overarching goal is to classify patient reported symptoms to organ systems for population health and epidemiological analyses.Materials and methodsUsing the concepts’ semantic types and the inter-concept relationships as guidance, a selective portion of the concepts within the UMLS Metathesaurus was traversed starting from the concepts representing the highest level organ systems. The traversed concepts were chosen, filtered, and reviewed to obtain the concepts representing clinical signs and symptoms by blocking deviations, pruning superfluous concepts, and manual review. The mapping process was applied to signs and symptoms annotated in a corpus of 750 clinical notes.ResultsThe mapping process yielded a total of 91,000 UMLS concepts (with approximately 300,000 descriptions) possibly representing physical and mental signs and symptoms that were extracted and categorized to the anatomically related organ systems. Of 1864 distinct descriptions of signs and symptoms found in the 750 document corpus, 1635 of these (88%) were successfully mapped to the set of concepts extracted from the UMLS. Of 668 unique concepts mapped, 603 (90%) were correctly categorized to their organ systems.ConclusionWe present a process that facilitates mapping of signs and symptoms to their organ systems. By providing a smaller set of UMLS concepts to use for comparing and matching patient records, this method has the potential to increase efficiency of information extraction pipelines

Meaningful Information Extraction from Unstructured Clinical Documents

Medical concept and entity extraction from the medical narrative unstructured documents is the crucial step in most of the contemporary health systems. For the extraction of medical concepts and entities, the Unified Medical Language System (UMLS) Metathesaurus is a big source of biomedical and health-related concepts. Recently various tools like Sophia, MetaMap and cTAKES, and many other rules-based methods and algorithm like Quick UMLS etc. have been developed which are performing a successful role in the process of medical concept extraction. The goal of this paper is to design a generic algorithm to identify a package consisting of standard concepts, their semantic types, and entity types on the basis of medical phrases and terms used in the clinical unstructured documents. The proposed algorithm implements the UMLS terminology service (UTS) and customizes to extract concepts for all the meaningful phrases and terms used in the narratives and determine their semantic and entity types in order to find exact categorization of the concepts. The proposed algorithm has produced a very useful set of results to use for labeling the biomedical data, which could in term be used for training data-driven approaches such asmachine learning

Deep learning approach to sentiment analysis in health and well-being

Sentiment analysis, also known as opinion mining, is an area of natural language processing which focuses on the classification of the sentiment that is expressed in a written document. Sentiment analysis has found applications in various domains including finance, politics, and health. This thesis is focused on sentiment analysis in the domain of health and well-being. An extensive systematic literature review was carried out to establish the state of the art in sentiment analysis in this domain. This systematic review provides evidence that the state-of-the-art results in sentiment analysis in the domain of health and well-being lags behind that in other domains. Additionally, it revealed that deep learning has not been used to classify the sentiment within the aforementioned domain. Furthermore, we performed a study and showed that the language that is used within the health and well-being domain is biased towards the negative sentiment. Aspect-based sentiment analysis refines the focus of sentiment analysis by classifying the sentiment associated with a specific aspect. Subsequently, we focus specifically on aspect-based sentiment analysis. To support it within the domain of health and well-being we created a dataset consisting of drug reviews, where the aspects were automatically annotated by matching concepts from the Unified Medical Language System. We have successfully shown that graph convolution can effectively utilise the context, represented with syntactic dependencies, to determine the intended sentiment of inherently negative aspects and consequently close the performance gap regardless of the domain. The advent of transformer-based architectures initiated a breakthrough in various tasks in natural language processing, including sentiment analysis. There-fore, we presented an approach to fine-tuning a transformer-based language model for the specific task of aspect-based sentiment analysis. The findings show the evidence that transformer-based models account for syntactic dependencies when classifying the sentiment of the given aspect