k-Neighborhood decentralization: A comprehensive solution to index the UMLS for large scale knowledge discovery

Xiang, Yang; Lu, Kai; James, Stephen; Borlawsky, Tara; Huang, Kun; Payne, Philip R. O.

doi:10.1016/j.jbi.2011.11.012

Cited by 13 publications

(15 citation statements)

References 34 publications

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“…The common nomenclature is a well-studied clinical terminology relationship generator and health domain taxonomy [36–38]. The common nomenclature would need to create a large number of new concepts to encompass the paradigm of nursing [44].…”

Section: Discussionmentioning

confidence: 99%

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Physician nurse care: A new use of UMLS to measure professional contribution

Boyd

Lopez

Lugaresi

et al. 2018

International Journal of Medical Informatics

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Background Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the physician perspective via the physician-authored discharge summary, but not nurse documentation. Prior research in this area has focused on collaboration and the usage of similar terminology. Objective The objective of the study is to gain insight into interprofessional care by developing a computational metric to identify similarities, related concepts and differences in physician and nurse work. Methods 58 physician discharge summaries and the corresponding nurse plans of care were transformed into Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). MedLEE, a Natural Language Processing (NLP) program, extracted “physician terms” from free-text physician summaries. The nursing plans of care were constructed using the HANDS© nursing documentation software. HANDS© utilizes structured terminologies: nursing diagnosis (NANDA-I), outcomes (NOC), and interventions (NIC) to create “nursing terms”. The physician’s and nurse’s terms were compared using the UMLS network for relatedness, overlaying the physician and nurse terms for comparison. Our overarching goal is to provide insight into the care, by innovatively applying graph algorithms to the UMLS network. We reveal the relationships between the care provided by each professional that is specific to the patient level. Results We found that only 26% of patients had synonyms (identical UMLS CUIs) between the two professions’ documentation. On average, physicians’ discharge summaries contain 27 terms and nurses’ documentation, 18. Traversing the UMLS network, we found an average of 4 terms related (distance less than 2) between the professions, leaving most concepts as unrelated between nurse and physician care. Conclusion Our hypothesis that physician’s and nurse’s practice domains are markedly different is supported by the preliminary, quantitative evidence we found. Leveraging the UMLS network and graph traversal algorithms, allows us to compare and contrast nursing and physician care on a single patient, enabling a more complete picture of patient care. We can differentiate professional contributions to patient outcomes and related and divergent concepts by each profession.

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Section: Discussionmentioning

confidence: 99%

“…The NLP system produces a semi-structured output where concepts are mapped to the corresponding CUI (concept unique identifier) terms from the UMLS metathesaurus, a “common nomenclature” [36–38]. A random sample of 58 de-identified physician discharge summaries was selected.…”

Section: Methodsmentioning

confidence: 99%

Physician nurse care: A new use of UMLS to measure professional contribution

Boyd

Lopez

Lugaresi

et al. 2018

International Journal of Medical Informatics

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“…There are a variety of methods that attempt to accomplish these tasks. Methods range from traversing the UMLS MRREL and MRHIER to discover relationships, to walking up individual ontologies parent-child hierarchies, to sophisticated methods such as k-Neighborhood decentralization [42][43][44][45][46]. Most of these solutions, however, are partial and do not necessarily perform the required tasks in a uniformly reliable way.…”

Section: Discussionmentioning

confidence: 99%

Lessons Learned from Developing a Drug Evidence Base to Support Pharmacovigilance

Smith¹,

Denny²,

Chen³

et al. 2013

Appl Clin Inform

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“…semantic features help identify synonymous medical concepts and the temporal feature helps identify time of occurrence). For the calculation of the semantic feature, k-Neighborhood decentralization method [145] seems to outperform breadth-first and depth-first searches between concepts in the UMLS graph structure[145]. KNDM can be used to index and transitively traverse associated relations between concept unique identifiers in the UMLS graph and reveal reachability, distance, and summary of paths, between two concepts in the UMLS graph structure.…”

Section: Temporal Processing In the Clinical Domainmentioning

confidence: 99%

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

Madkour

Benhaddou

Tao

2016

Computer Methods and Programs in Biomedicine

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Background and Objective We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic Health Records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives. Methods This review surveys the methods used in three important area: modeling and representing of time, Medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations. Results the main findings of this review is revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations. Conclusions Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

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k-Neighborhood decentralization: A comprehensive solution to index the UMLS for large scale knowledge discovery

Cited by 13 publications

References 34 publications

Physician nurse care: A new use of UMLS to measure professional contribution

Physician nurse care: A new use of UMLS to measure professional contribution

Lessons Learned from Developing a Drug Evidence Base to Support Pharmacovigilance

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

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