Use of the Unified Medical Language System in Patient Care at the Columbia-Presbyterian Medical Center

1995 ◽  
Vol 34 (01/02) ◽  
pp. 158-64 ◽  
Author(s):  
J. J. Cimino
Keyword(s):  
Patient Care ◽  
Structural Model ◽  
Clinical Information System ◽  
Medical Center ◽  
Clinical Information ◽  
The United States ◽  
Semantic Relations ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language

Abstract:The Unified Medical Language System (UMLS) project at the United States National Library of Medicine contains and organizes a large number of terms from controlled medical vocabularies. This study examines the suitability of the UMLS for representing patient care information as it exists in the Columbia-Presbyterian Medical Center (CPMC) clinical information system. Comparisons were made between the semantic types, semantic relations and medical concepts of the UMLS and the data model entities, semantic classes, semantic relations and concepts in the CPMC system. Results of the comparison demonstrate that the UMLS structural model is appropriate for representing CPMC vocabularies and patient data and that the UMLS concepts provide excellent coverage of CPMC concepts in many areas. Recommendations are made for enhancing UMLS structure to provide additional coverage of the CPMC model. It is concluded that content expansion to provide better coverage of clinical terminology is possible within the current UMLS model.

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.

Sistem Integrasi Aplikasi Desktop Klinik MCU TKI Pada Medical Center Purwokerto

2020 ◽  
Vol 2 (01) ◽  
pp. 1-8
Author(s):  
Lutfi Syafirullah ◽  
Hidayat Muhammad Nur ◽  
Vadlya Ma'arif
Keyword(s):  
Network Architecture ◽  
Clinical Information System ◽  
Medical Center ◽  
Current System ◽  
Clinical Information ◽  
Clinical Work ◽  
Database Systems ◽  
Prototype Model ◽  
Medical Checkup ◽  
Operational Activities

Information technology integration is expected to be able to accommodate the ease and improvement in supporting database platforms through intranet and internet infrastructure. Integration is intended to blend desktop and web database systems. Medical Checkup Purwokerto is a designated place to facilitate the checkup health of the official PJTKI Banyumas Disnaker BNP2TKI. The current system, which is a check-up application, is carried out by prospective Indonesian Workers or Medical checkup units, covering many processes including registration, health checks, types, results, payments and reports. There was a buildup of operational activities Clinical work on a daily basis, by the administrator of the medical record so that management aimed at developing a web-based clinical information system includes the scope of the processed database components, access authorization, and security. The method used is the software development life cycle (SDLC) with the Evolutionary Prototype Model. Results, patient data can be integrated as a whole process flow with a client-server network architecture

scholarly journals Medical Informatics Training at Columbia University and the Columbia-Presbyterian Medical Center

1995 ◽  
Vol 04 (01) ◽  
pp. 125-129
Author(s):  
B. A. Allen ◽  
P. D. Clayton ◽  
J. J. Cimino
Keyword(s):  
Medical Informatics ◽  
Medical Center ◽  
Clinical Information ◽  
Information Management System ◽  
Advanced Technology ◽  
Columbia University ◽  
Postdoctoral Training ◽  
Unified Medical Language System ◽  
Part Time Faculty ◽  
Part Time

Abstract:The Department of Medical Informatics at Columbia University College of Physicians and Surgeons consists of a faculty of 17 full-and part-time faculty. The Department faculty collaborate with the Department of Computer Science and several clinical departments of the medical center. We offer courses in medical informatics, formal degrees (M.A., M.Phil. and Ph.D.) and a postdoctoral training program. In addition to academic offerings, the close affiliation with the Columbia-Presbyterian Medical Center and the primary responsibilities for clinical information systems offers trainees unique opportunities to work with and develop real-world applications. Faculty research programs include work on the Integrated Advanced Information Management System (IAIMS), Unified Medical Language System (UMLS), High-Perfor-mance Computing and Communications (HPCC), Electronic Medical Records, automated decision support and technology transfer through the Center for Advanced Technology.

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.

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