Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS)

Patil, Meru A.; Bhaumik, Sandip; Paul, Soubhik; Bissoyi, Swarupananda; Roy, Raj; Ryu, Seungwoo

doi:10.1109/embc.2013.6609740

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…The RIP can be expressed as below. Knowlife KG [50] 7 13 50 000 609 322 MKG [47] 9 -22 508 579 094 HG [31] -14 400 000 200 000 SMR KG [66] 4 4 367 201 1 707 609 Disease oriented CLKG [127] --200 000 1 000 000 Geriatric KG [26] 6 7 --Depression KG [79] ---8 892 722 Knee osteoarthritis KG [65] 8 10 2518 29 972 SemKG [92] 5 4 --DSTKG [130] 7 16 9868 11 005 Cancer KG [15] ---90 673 527 KGHC [98] 10 22 5028 13 296 Rare disease KG [94] 10 42-3 819 623 84 223 681 COVID-KOP [27] --45 300 5 532 000 DRKF KG [49] -43 12 497 165 901 LT-D DB [51] 3 11 --KDKG [13] ---10 146 311 StrokeKG [91] 9 30 46 000 157 000 KGPA [71] 11 10 --…”

Section: Reasoning Over Mkgmentioning

confidence: 99%

“…Given these properties, graph theory-based algorithms may be used effortlessly in knowledge reasoning [133] . Patil et al [51] developed a CG-Engine that treats MKGs as graphs. The graph's edges are weighted, and the disease's risk value is calculated by computing the comprehensive weight value of the edge connected to each disease node.…”

Section: Reasoning Based On Gmmentioning

confidence: 99%

“…attentation: classification Identify potential migraine biomarkers [82] -SP: extract compounds ! filter&rank: ranking Multi-disease diagnosis [136] CEMRs KG KRL: RNKN: classification Decision support for UTI [3] -SP: EYE(BBNs&FCMs): ranking Provide personalized disease ranking [51] LT-D DB GM: CG-Engine: ranking Predicate subarachnoid hemorrhage [23] -SP: rule&logic: relation prediction…”

Section: Disease Diagnosismentioning

confidence: 99%

“…query: visualization& KBQA Health risk prediction [14] HKG GM: optimisation algorithm: classification Predicting the status of health risks [46] KB-HIG KRL: graph weighting ! Word2Vec: classification Estimating personalized risk ranking [51] LT-D DB KRL: CG-Engine: ranking Others Fraud, waste, and abuse (FWA) detection [45] FWA KG SP: rule inference: relation prediction Deep patient similarity [67] DeepPS KG KRL: KGE ! CNN: similarity Automated ICD coding [55] G Coder KG KRL: SDNE !…”

Section: Intelligent Nursingmentioning

confidence: 99%

“…Standard libraries are a kind of standard and shared medical terminology and standards, which include the Medical Subject Headings (MeSH) [16] , Unified Medical Language System (UMLS) [18] , International Classification of Diseases (ICD), SNOMED-CT [17] , and so on; Zhang et al [29] created an obstetric KG based on the hierarchical structure of MeSH [16] as the ontology prototype. Patil et al [51] developed a concept graph engine (CG-Engine) and used the UMLS database as its medical knowledge base.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

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Section: Reasoning Over Mkgmentioning

confidence: 99%

Section: Reasoning Based On Gmmentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

Section: Intelligent Nursingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Duan

Pan

et al. 2023

Big Data Min. Anal.

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Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes

Gao

Wang³

et al. 2019

BMC Med Inform Decis Mak

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Background Electronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors’ decision making. The research on information extraction of Chinese Electronic Medical Records is still behind that has done in English. Methods This paper proposed a practical annotation scheme for medical entity extraction on Resident Admit Notes (RANs), and a model which can automatic extract medical entity. Nine types of clinical entities, four types of clinical relationships were defined in our annotation scheme. An end-to-end deep neural network with convolution neural network and long-short term memory units was applied in our model for the medical named entity recognition(NER). Result We annotated RANs in three rounds. The overall F-score of annotation consistency was up to 97.73%. And our NER model on the 255 annotated RANs achieved the best F-score of 91.08%. Conclusion The annotation scheme and the model for NER in this paper are effective to extract medical named entity from RANs and provide the basis for fully excavating the patient’s information.

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The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

Jing¹

2021

JMIR Med Inform

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Background The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications. Objective Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years. Methods PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Results A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%). Conclusions The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

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Estimating personalized risk ranking using laboratory test and medical knowledge (UMLS)

Cited by 4 publications

References 5 publications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

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