Semantics Driven Approach for Knowledge Acquisition From EMRs

Perera, Sujan; Henson, Cory; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Nair, Suhas

doi:10.1109/jbhi.2013.2282125

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…We set average support and average confidence as minimum thresholds to pick top associations. We leveraged the increment of explanatory power (IEP) [36] to quantify the enrichment on HPO-Orphanet.…”

Section: Resultsmentioning

confidence: 99%

Rare disease knowledge enrichment through a data-driven approach

Shen

Zhao

Wang

et al. 2019

BMC Med Inform Decis Mak

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Background Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). Methods We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs. Results We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer. Conclusions Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis. Electronic supplementary material The online version of this article (10.1186/s12911-019-0752-9) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Rare disease knowledge enrichment through a data-driven approach

Shen

Zhao

Wang

et al. 2019

BMC Med Inform Decis Mak

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“…We observe that although the existing medical knowledge bases (e.g., Unified Medical Language System (UMLS)) are rich in taxonomical relationships, they lack non-taxonomical relationships among clinical entities. We have developed data-driven algorithms that use real-world clinical data (such as EMRs) to discover missing relationships between clinical entities in existing knowledge base, and then get these validated by a domain-expert-in-the-loop [24]. Yet another challenge is creating personalized knowledge bases for specific tasks.…”

Section: Challenges In Creating and Using Knowledge Basesmentioning

confidence: 99%

Knowledge will propel machine understanding of content

Sheth

Perera

Wijeratne

et al. 2017

Proceedings of the International Conference on Web Intelligence

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Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

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“…After HISs have become common, many efforts are ongoing to efficiently represent medical information in EMR [1,2] and to efficiently manage them [3,4]. Some previous works presented analysis or processing methods [5][6][7][8] and transfer or exchange systems [9,10] for medical images.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays most of medical institutions like hospitals naturally use HIS. Recently, many studies [1][2][3][4][5][6][7][8][9][10] have tried to efficiently store, transfer, and manage clinical materials. However, HIS consists of several systems such as Order Communication System (OCS), Electronic Medical Record (EMR), and Picture Archiving and Communication System (PACS) and they generate clinical materials of different types.…”

Section: Introductionmentioning

confidence: 99%

Image-Centric Integrated Data Model of Medical Information by Diseases: Two Case Studies for AMI and Ischemic Stroke

2016

J Inf Process Syst

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In the medical fields, many efforts have been made to develop and improve Hospital Information System (HIS) including Electronic Medical Record (EMR), Order Communication System (OCS), and Picture Archiving and Communication System (PACS). However, materials generated and used in medical fields have various types and forms. The current HISs separately store and manage them by different systems, even though they relate to each other and contain redundant data. These systems are not helpful particularly in emergency where medical experts cannot check all of clinical materials in the golden time. Therefore, in this paper, we propose a process to build an integrated data model for medical information currently stored in various HISs. The proposed data model integrates vast information by focusing on medical images since they are most important materials for the diagnosis and treatment. Moreover, the model is disease-specific to consider that medical information and clinical materials including images are different by diseases. Two case studies show the feasibility and the usefulness of our proposed data model by building models about two diseases, acute myocardial infarction (AMI) and ischemic stroke.

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Semantics Driven Approach for Knowledge Acquisition From EMRs

Cited by 17 publications

References 12 publications

Rare disease knowledge enrichment through a data-driven approach

Rare disease knowledge enrichment through a data-driven approach

Knowledge will propel machine understanding of content

Image-Centric Integrated Data Model of Medical Information by Diseases: Two Case Studies for AMI and Ischemic Stroke

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