Dataset Information

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

ABSTRACT: BACKGROUND:Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate descriptive data to use NLP. METHODS:Among VA patients who underwent stress MPI and coronary angiography between January 1, 2009 and December 31, 2011, 99 stress test reports were randomly selected for analysis. Two reviewers independently categorized each report for the presence of critical data elements essential to describing post-test ischemic risk. RESULTS:Few stress MPI reports provided a formal assessment of post-test risk within the impression section (3%) or the entire document (4%). In most cases, risk was determinable by combining critical data elements (74% impression, 98% whole). If ischemic risk was not determinable (25% impression, 2% whole), inadequate description of systolic function (9% impression, 1% whole) and inadequate description of ischemia (5% impression, 1% whole) were most commonly implicated. CONCLUSIONS:Post-test ischemic risk was determinable but rarely reported in this sample of stress MPI reports. This supports the potential use of NLP to help clarify risk. Further study of NLP in this context is needed.

SUBMITTER: Levy AE

PROVIDER: S-EPMC6202272 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Levy Andrew E AE Shah Nishant R NR Matheny Michael E ME Reeves Ruth M RM Gobbel Glenn T GT Bradley Steven M SM

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20180425 6

<h4>Background</h4>Reporting standards promote clarity and consistency of stress myocardial perfusion imaging (MPI) reports, but do not require an assessment of post-test risk. Natural Language Processing (NLP) tools could potentially help estimate this risk, yet it is unknown whether reports contain adequate descriptive data to use NLP.<h4>Methods</h4>Among VA patients who underwent stress MPI and coronary angiography between January 1, 2009 and December 31, 2011, 99 stress test reports were ra ...[more]

PMID: 29696484

Similar Datasets

Project description:BackgroundNatural language processing (NLP) has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses and quantifies recent literature in NLP applied to radiology reports.MethodsWe conduct an automated literature search yielding 4836 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics.ResultsWe present a comprehensive analysis of the 164 publications retrieved with publications in 2019 almost triple those in 2015. Each publication is categorised into one of 6 clinical application categories. Deep learning use increases in the period but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results.ConclusionsAutomated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process and we show that research in this field continues to grow. Reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers in the field providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

Project description:BackgroundSilent brain infarction (SBI) is defined as the presence of 1 or more brain lesions, presumed to be because of vascular occlusion, found by neuroimaging (magnetic resonance imaging or computed tomography) in patients without clinical manifestations of stroke. It is more common than stroke and can be detected in 20% of healthy elderly people. Early detection of SBI may mitigate the risk of stroke by offering preventative treatment plans. Natural language processing (NLP) techniques offer an opportunity to systematically identify SBI cases from electronic health records (EHRs) by extracting, normalizing, and classifying SBI-related incidental findings interpreted by radiologists from neuroimaging reports.ObjectiveThis study aimed to develop NLP systems to determine individuals with incidentally discovered SBIs from neuroimaging reports at 2 sites: Mayo Clinic and Tufts Medical Center.MethodsBoth rule-based and machine learning approaches were adopted in developing the NLP system. The rule-based system was implemented using the open source NLP pipeline MedTagger, developed by Mayo Clinic. Features for rule-based systems, including significant words and patterns related to SBI, were generated using pointwise mutual information. The machine learning models adopted convolutional neural network (CNN), random forest, support vector machine, and logistic regression. The performance of the NLP algorithm was compared with a manually created gold standard. The gold standard dataset includes 1000 radiology reports randomly retrieved from the 2 study sites (Mayo and Tufts) corresponding to patients with no prior or current diagnosis of stroke or dementia. 400 out of the 1000 reports were randomly sampled and double read to determine interannotator agreements. The gold standard dataset was equally split to 3 subsets for training, developing, and testing.ResultsAmong the 400 reports selected to determine interannotator agreement, 5 reports were removed due to invalid scan types. The interannotator agreements across Mayo and Tufts neuroimaging reports were 0.87 and 0.91, respectively. The rule-based system yielded the best performance of predicting SBI with an accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.991, 0.925, 1.000, 1.000, and 0.990, respectively. The CNN achieved the best score on predicting white matter disease (WMD) with an accuracy, sensitivity, specificity, PPV, and NPV of 0.994, 0.994, 0.994, 0.994, and 0.994, respectively.ConclusionsWe adopted a standardized data abstraction and modeling process to developed NLP techniques (rule-based and machine learning) to detect incidental SBIs and WMDs from annotated neuroimaging reports. Validation statistics suggested a high feasibility of detecting SBIs and WMDs from EHRs using NLP.

Dataset Information

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Publications

Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: Implications for natural language processing tools.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets