Ontological Approach for Knowledge Extraction from Clinical Documents In clinical NLP (Natural Language Processing), Knowledge extraction is a very important task to develop a highly accurate information retrieval system. The various approaches used to develop such systems include rulebased approach, statistical approach, shortest path algorithm or hybrid of these approaches. Accuracy and coverage are the most important parameters while comparing different approaches. Some methodologies have good accuracy but low coverage and viceversa. In this paper, our focus is to extract domain relationships, for example to extract the relationship between xe2x80x98Diseasexe2x80x99 and xe2x80x98Procedurexe2x80x99 or xe2x80x98Symptomxe2x80x99 and xe2x80x98Diseasexe2x80x99 etc. from the clinical documents using three different approaches. These three approaches are i) Statistical ii) Shortest Path iii) Shortest Path Using Body System. All three approaches use our inhouse existing NLP system to extract entities from the unstructured documents. The Statistical approach applies a probabilistic algorithm on clinical documents, whereas the Shortest Path algorithm uses the Ontological knowledge base for the hierarchical relationship between entities. This Ontological knowledge base is built upon the curated Unified Medical Language System (UMLS). For the Shortest Path Using Body System approach, we have used the domain relationship as well as hierarchical relationship. The output of these approaches is further validated by a domain expert and this validated relationship is used to enrich our ontological knowledge base. We have presented the details of these approaches onebyone along with the comparative results of these approaches. We finally go through the analysis of the result and conclude on further work.

Multiple features for clinical relation extraction: A machine learning approach Abstract   Relation extraction aims to discover relational facts about entity mentions from plain texts. In this work, we focus on clinical relation extraction; namely, given a medical record with mentions of drugs and their attributes, we identify relations between these entities. We propose a machine learning model with a novel set of knowledgebased and BioSentVec embedding features. We systematically investigate the impact of these features with standard distance and wordbased features, conducting experiments on two benchmark datasets of clinical texts from MADE 2018 and n2c2 2018 shared tasks. For comparison with the featurebased model, we utilize stateoftheart models and three BERTbased models, including BioBERT and Clinical BERT. Our results demonstrate that distance and word features provide significant benefits to the classifier. Knowledgebased features improve classification results only for particular types of relations. The sentence embedding feature provides the largest improvement in results, among other explored features on the MADE corpus. The classifier obtains stateoftheart performance in clinical relation extraction with Fmeasure of 92.6%, improving Fmeasure by 3.5% on the MADE corpus.