Text mining brain imaging reports

Alex, Beatrice; Grover, Claire; Tobin, Richard; Sudlow, Catherine; Mair, Grant; Whiteley, William

doi:10.1186/s13326-019-0211-7

Cited by 26 publications

(22 citation statements)

References 18 publications

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“…Previous studies applying NLP and machine learning to classify stroke into subtypes have focused on automating ischemic stroke subtyping into specific sub-categories using the EMR [ 30 , 31 ] or a selection of available features [ 32 ]. Others, such as the Edinburgh Information Extraction for Radiology reports (EdIE-R) [ 33 ] have shown good performance of text mining systems in subtyping already expert-validated stroke cases into the three main subtypes (IS, ICH and SAH) based on radiology scan reports. Our study differs from these in two main ways.…”

Section: Discussionmentioning

confidence: 99%

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Rannikmäe¹,

Tominey

et al. 2021

BMC Med Inform Decis Mak

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Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping. Methods From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels. Results Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype. Conclusions Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting.

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Section: Discussionmentioning

confidence: 99%

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Rannikmäe¹,

Tominey

et al. 2021

BMC Med Inform Decis Mak

Self Cite

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“…EdIE-R [11] is a rule-based system which has also been designed to label radiology reports for head CT scans from stroke patients [33]. However, the labels that the rules were created for are slightly different so, in order to compare this model with ours, we have mapped the EdIE-R labels to a subset of our labels as follows: Ischaemic stroke to Infarct/Ischaemia; Haemorrhagic stroke and Haemorrhagic transformation to Haemorrhage/Haematoma; Cerebral small vessel disease, Tumour and Atrophy to our identical labels.…”

Section: Comparison Of the Proposed Methods With A Rules-based Systemmentioning

confidence: 99%

“…On inspection, we observe that the EdIE-R system labels any mentions of "mass" as a tumour, while, in our system, a mass is only labelled as a tumour if there is a specific mention of "tumour" or subtype of tumour (e.g., "meningioma"); otherwise, we label as a (non-specific) Lesion. It is therefore likely that Tumour label is defined differently between our annotation protocol and the protocol used by Alex et al [33].…”

Section: Comparison Of the Proposed Methods With A Rules-based Systemmentioning

confidence: 99%

Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

Schrempf

Watson

Park

et al. 2021

MAKE

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Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

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“…This category was not found in Pons' work. Methods considered a range of conditions including intracranial haemorrhage [43,44], aneurysms [45], brain metastases [46], ischaemic stroke [47,48], and several classified on types and severity of conditions e.g., [46,[49][50][51][52]. Studies focused on breast imaging considered aspects such as predicting lesion malignancy from BI-RADS descriptors [53], breast cancer subtypes [54], and extracting or inferring BI-RADS categories, such as [55,56].…”

Section: Disease Information and Classificationmentioning

confidence: 99%

A systematic review of natural language processing applied to radiology reports

Casey

Davidson

Poon

et al. 2021

BMC Med Inform Decis Mak

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Background Natural 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. Methods We 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. Results We 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. Conclusions Automated 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.

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Text mining brain imaging reports

Cited by 26 publications

References 18 publications

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

A systematic review of natural language processing applied to radiology reports

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