Using the Unified Medical Language System to Expand the Operative Stress Score – First Use Case

2021 ◽  
Vol 268 ◽  
pp. 552-561
Author(s):  
Katherine M Reitz ◽  
Daniel E Hall ◽  
Myrick C Shinall ◽  
Paula K Shireman ◽  
Jonathan C Silverstein
Keyword(s):  
Use Case ◽  
Language System ◽  
Unified Medical Language System ◽  
Operative Stress ◽  
Medical Language ◽  
Stress Score

IAIMS and UMLS at Columbia-Presbyterian Medical Center

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S89-S93 ◽  
Author(s):  
James J. Cimino ◽  
Soumitra Sengupta
Keyword(s):  
Information Management ◽  
Management System ◽  
Medical Center ◽  
Information Management System ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Academic Information

The authors use an example to illustrate combining Integrated Academic Information Management System (IAIMS) components (applications) into an integral whole, to facilitate using the components simultaneously or in sequence. They examine a model for classifying IAIMS systems, proposing ways in which the Unified Medical Language System (UMLS) can be exploited in them.

scholarly journals Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts

2020 ◽  
Vol 27 (10) ◽  
pp. 1538-1546 ◽  
Author(s):  
Yuqing Mao ◽  
Kin Wah Fung
Keyword(s):  
Word Sense Disambiguation ◽  
Graph Embedding ◽  
Semantic Relatedness ◽  
Word Sense ◽  
Medical Subject Headings ◽  
Network Graph ◽  
Convolutional Network ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language

Abstract Objective The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. Materials and Methods Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus. Results Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec. Conclusions Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

scholarly journals Can Unified Medical Language System–based semantic representation improve automated identification of patient safety incident reports by type and severity?

2020 ◽  
Vol 27 (10) ◽  
pp. 1502-1509 ◽  
Author(s):  
Ying Wang ◽  
Enrico Coiera ◽  
Farah Magrabi
Keyword(s):  
Patient Safety ◽  
Reporting System ◽  
Semantic Features ◽  
Automated Identification ◽  
Exact Match ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Extreme Risk ◽  
Incident Reports

Abstract Objective The study sought to evaluate the feasibility of using Unified Medical Language System (UMLS) semantic features for automated identification of reports about patient safety incidents by type and severity. Materials and Methods Binary support vector machine (SVM) classifier ensembles were trained and validated using balanced datasets of critical incident report texts (n_type = 2860, n_severity = 1160) collected from a state-wide reporting system. Generalizability was evaluated on different and independent hospital-level reporting system. Concepts were extracted from report narratives using the UMLS Metathesaurus, and their relevance and frequency were used as semantic features. Performance was evaluated by F-score, Hamming loss, and exact match score and was compared with SVM ensembles using bag-of-words (BOW) features on 3 testing datasets (type/severity: n_benchmark = 286/116, n_original = 444/4837, n_independent =6000/5950). Results SVMs using semantic features met or outperformed those based on BOW features to identify 10 different incident types (F-score [semantics/BOW]: benchmark = 82.6%/69.4%; original = 77.9%/68.8%; independent = 78.0%/67.4%) and extreme-risk events (F-score [semantics/BOW]: benchmark = 87.3%/87.3%; original = 25.5%/19.8%; independent = 49.6%/52.7%). For incident type, the exact match score for semantic classifiers was consistently higher than BOW across all test datasets (exact match [semantics/BOW]: benchmark = 48.9%/39.9%; original = 57.9%/44.4%; independent = 59.5%/34.9%). Discussion BOW representations are not ideal for the automated identification of incident reports because they do not account for text semantics. UMLS semantic representations are likely to better capture information in report narratives, and thus may explain their superior performance. Conclusions UMLS-based semantic classifiers were effective in identifying incidents by type and extreme-risk events, providing better generalizability than classifiers using BOW.

scholarly journals Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource

2020 ◽  
Vol 27 (8) ◽  
pp. 1310-1315 ◽  
Author(s):  
Abeed Sarker ◽  
Sahithi Lakamana ◽  
Whitney Hogg-Bremer ◽  
Angel Xie ◽  
Mohammed Ali Al-Garadi ◽  
...  
Keyword(s):  
Clinical Studies ◽  
Future Research ◽  
Clinical Settings ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language ◽  
Self Reports ◽  
Body Ache ◽  
Standard Concept

Abstract Objective To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. Materials and Methods We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. Results We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. Conclusion The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.

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