Comparison of UMLS terminologies to identify risk of heart disease using clinical notes

Shivade, Chaitanya; Malewadkar, Pranav; Fosler‐Lussier, Eric; Lai, Albert M.

doi:10.1016/j.jbi.2015.08.025

Cited by 29 publications

(24 citation statements)

References 24 publications

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“…The team from The Ohio State University (Shivade et al, this issue), ranked 6 th , identified concepts in the training data, belonging to a variety of openly available terminologies. They used these concepts to trigger rules consisting of regular expressions and UMLS concepts.…”

Section: Submissionsmentioning

confidence: 99%

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Stubbs

Kotfila

et al. 2015

Journal of Biomedical Informatics

107

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The second track of the 2014 i2b2/UTHealth Natural Language Processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk factors included hypertension, hyperlipidemia, obesity, smoking status, and family history, as well as diabetes and CAD, and indicators that suggest the presence of those diseases. In addition to identifying the risk factors, this track of the 2014 i2b2/UTHealth shared task studied the presence and progression of the risk factors in longitudinal medical records. Twenty teams participated in this track, and submitted 49 system runs for evaluation. Six of the top 10 teams achieved F1 scores over 0.90, and all 10 scored over 0.87. The most successful system used a combination of additional annotations, external lexicons, hand-written rules and Support Vector Machines. The results of this track indicate that identification of risk factors and their progression over time is well within the reach of automated systems.

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Section: Submissionsmentioning

confidence: 99%

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Stubbs

Kotfila

et al. 2015

Journal of Biomedical Informatics

107

View full text Add to dashboard Cite

show abstract

“…The study of Shivade et al [51] includes two strategies; first, a rule-based strategy that relies on information as the length of a string when it is a section heading, the usage of camel case, and the set of commonly used words that constitute a section heading. The second one is focused on implicit sections.…”

Section: Resultsmentioning

confidence: 99%

Current approaches to identify sections within clinical narratives from electronic health records: a systematic review

Pomares-Quimbaya

Kreuzthaler

Schulz

2019

BMC Med Res Methodol

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Background The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods. Methods This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment. Results Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus. Conclusions Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.

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“…Dollah and Aono have introduced ontology-based classification approaches for biomedical abstract text classification [9]. Authors in [10][11][12][13] Medical documents have been utilized in different tasks such as analyzing Framingham risk score (FRF), assessing risk factors in diabetic patients, discriminating heart disease risk factors, and finding risk factors for heart disease patients [14]. In this paper, we employ ontology as a feature extraction approach to detect meaningful words and expressions for augmenting documents.…”

Section: Feature Extraction In Medical Document Classificationmentioning

confidence: 99%

Ontology-Guided Data Augmentation for Medical Document Classification

Abdollahi¹,

Xiaoying²,

Yi³

et al. 2020

Preprint

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Extracting meaningful features from unstructured text is one of the most challenging tasks in medical document classification. The various domain specific expressions and synonyms in the clinical discharge notes make it more challenging to analyse them. The case becomes worse for short texts such as abstract documents. These challenges can lead to poor classification accuracy. As the medical input data is often not enough in the real world, in this work a novel ontology-guided method is proposed for data augmentation to enrich input data. Then, three different deep learning methods are employed to analyse the performance of the suggested approach for classification. The experimental results show that the suggested approach achieved substantial improvement in the targeted medical documents classification.

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Comparison of UMLS terminologies to identify risk of heart disease using clinical notes

Cited by 29 publications

References 24 publications

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2

Current approaches to identify sections within clinical narratives from electronic health records: a systematic review

Ontology-Guided Data Augmentation for Medical Document Classification

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