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|Date||Headline (link)||Source||Relevant Snippet|
|12/5/2019||Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text||... our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications. Background Clinical narratives are rich with patients’ clinical information such as disorders ... encode them into standard concepts in ontologies such as the UMLS (Unified Medical Language System). However, downstream clinical applications, such as clinical decision support systems, often require additional attribute information of medical concepts. For example ...|
|11/12/2019||Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes||... datasets, resulting in 880 term pairs (target term, candidate term). Each concept, represented by an n-gram, is mapped to UMLS using MetaMap; we also developed a bespoke method for mapping short forms (e.g. abbreviations ... task and advanced abbreviation recognition modules are needed for existing clinical NLP systems ”. Dealing with short forms is therefore a challenge that requires an approach to deal with terms appearing in both biomedical and clinical ...|
|4/25/2019||Looking for Clinical NLP dev (experience working with UMLS and its lexicon tools)||Java Looking for Clinical NLP dev (experience working with UMLS and its lexicon tools) We are working on a clinical NLP project that uses the UMLS vocabulary and tools - and are looking to grow the solution beyond simple concept matching. The first core requirement is to utilize UMLS lexicon tools to improve the accuracy/sensitive of our extractions, but this project will be long term. We are ideally looking for ...|