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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 a rule-based approach, a statistical approach, shortest path algorithm, or a hybrid of these approaches. Read EZDI’s research paper to learn more about the “Ontological Approach for Knowledge Extraction from Clinical Documents”.
Creation of Unambiguous Centralized Knowledge Base from UMLS Metathesaurus
In this paper, we describe our methodology of curating the UMLS metathesaurus to create a centralized knowledge base that can be used as a knowledge base for a variety of clinical NLP systems. We have also developed a process of updating the curated centralized knowledge base with a newer version of UMLS such that there is no need to repeat the whole process.
Annotation of a Large Clinical Entity Corpus
In this paper, we have described in detail the annotation guidelines, annotation process and our approaches in creating a CER (clinical entity recognition) corpus of 5,160 clinical documents from forty different clinical specialties. Published at the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-2018), pp. 2033-2042. 2018.
Relation extraction between the clinical entities based on the shortest dependency path based LSTM
In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence. It will be published at @ICON 2018.
Automated Clinical Documentation Improvement
Complete and accurate clinical documentation in the medical record has a direct impact on the assignment of codes, more accurate levels of reimbursement, and is critical to the higher quality of patient care. This paper describes the development of a system which can automatically flag the cases if there is an opportunity for improvement in patient clinical documents.