Deep Neural Models for Medical Concept Normalization in User-Generated Texts

In this work, we consider the medical concept normalization problem, i.e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS). This is a challenging task since medical terminology is very different when coming from health care professionals or from the general public in the form of social media texts. We approach it as a sequence learning problem with powerful neural networks such as recurrent neural networks and contextualized word representation models trained to obtain semantic representations of social media expressions. Our experimental evaluation over three different benchmarks shows that neural architectures leverage the semantic meaning of the entity mention and significantly outperform an existing state of the art models.Comment: This is preprint of the paper "Deep Neural Models for Medical Concept Normalization in User-Generated Texts" to be published at ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Worksho

Ontology-Based Clinical Information Extraction Using SNOMED CT

Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction. In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing

Automatic Job Skill Taxonomy Generation For Recruitment Systems

The goal of this thesis is to optimize the job recommendation systems by automatically extracting the skills from the job descriptions. With rapid development in technology, new skills are continuously required. This makes the skill tagging of the job descriptions a more difficult problem since a simple keyword match from an already generated skill list is not suitable. A way of automatically populating the skills list to improve the job search engines is needed. This thesis focuses on solving this problem with the help of natural language processing and neural networks. Automatic detection of skills in the unstructured job description dataset is a complex problem as it involves being robust to the ambiguity of natural language and adapting to words not seen in the historical data. This thesis solves this problem by using recurrent neural network models for capturing the context of the skill words. Based on the context captured, the new system is capable of predicting if the word in the given text is a skill or not. Neural network models like Long short-term memory and Bi-directional Long short-term memory are used to capture the long term dependencies in the sentence to identify skills present in the job descriptions. Various natural language processing techniques were utilized to improve the input feature quality to the model. Results obtained from using context before and after the skill words have shown the best results in identifying skills from textual data. This can be applied to capture skills data from job ads as well as it can be extended to extract the skill features from resume data to improve the job recommendation results in the future