Toward diversity, equity, and inclusion in informatics, health care, and society.
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaa265
Author(s): Bakken, Suzanne
DOI: 10.1093/jamia/ocaa265
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.
Author(s): Wang, Ying, Coiera, Enrico, Magrabi, Farah
DOI: 10.1093/jamia/ocaa082
The Unified Medical Language System (UMLS) is 1 of the most successful, collaborative efforts of terminology resource development in biomedicine. The present study aims to 1) survey historical footprints, emerging technologies, and the existing challenges in the use of UMLS resources and tools, and 2) present potential future directions.
Author(s): Kim, Meen Chul, Nam, Seojin, Wang, Fei, Zhu, Yongjun
DOI: 10.1093/jamia/ocaa107
Author(s): Humphreys, Betsy L, Del Fiol, Guilherme, Xu, Hua
DOI: 10.1093/jamia/ocaa208
Normalizing clinical mentions to concepts in standardized medical terminologies, in general, is challenging due to the complexity and variety of the terms in narrative medical records. In this article, we introduce our work on a clinical natural language processing (NLP) system to automatically normalize clinical mentions to concept unique identifier in the Unified Medical Language System. This work was part of the 2019 n2c2 (National NLP Clinical Challenges) Shared-Task and [...]
Author(s): Chen, Long, Fu, Wenbo, Gu, Yu, Sun, Zhiyong, Li, Haodan, Li, Enyu, Jiang, Li, Gao, Yuan, Huang, Yang
DOI: 10.1093/jamia/ocaa155
We explored how knowledge embeddings (KEs) learned from the Unified Medical Language System (UMLS) Metathesaurus impact the quality of relation extraction on 2 diverse sets of biomedical texts.
Author(s): Weinzierl, Maxwell A, Maldonado, Ramon, Harabagiu, Sanda M
DOI: 10.1093/jamia/ocaa205
The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.
Author(s): Mao, Yuqing, Fung, Kin Wah
DOI: 10.1093/jamia/ocaa136
The 2019 National Natural language processing (NLP) Clinical Challenges (n2c2)/Open Health NLP (OHNLP) shared task track 3, focused on medical concept normalization (MCN) in clinical records. This track aimed to assess the state of the art in identifying and matching salient medical concepts to a controlled vocabulary. In this paper, we describe the task, describe the data set used, compare the participating systems, present results, identify the strengths and limitations [...]
Author(s): Henry, Sam, Wang, Yanshan, Shen, Feichen, Uzuner, Ozlem
DOI: 10.1093/jamia/ocaa106
Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system.
Author(s): Reimer, Andrew P, Milinovich, Alex
DOI: 10.1093/jamia/ocaa176
We sought to assess the need for additional coverage of dietary supplements (DS) in the Unified Medical Language System (UMLS) by investigating (1) the overlap between the integrated DIetary Supplements Knowledge base (iDISK) DS ingredient terminology and the UMLS and (2) the coverage of iDISK and the UMLS over DS mentions in the biomedical literature.
Author(s): Vasilakes, Jake, Bompelli, Anusha, Bishop, Jeffrey R, Adam, Terrence J, Bodenreider, Olivier, Zhang, Rui
DOI: 10.1093/jamia/ocaa128