Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods

Trivedi, Gaurav; Hong, Charmgil; Dadashzadeh, Esmaeel R.; Handzel, Robert; Hochheiser, Harry

doi:10.1016/j.ijmedinf.2019.05.021

Cited by 12 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies in this category also investigated incidental findings including on lung imaging [38][39][40], with [38] additionally extracting the nodule size; for trauma patients [41]; and looking for silent brain infarction and white matter disease [42]. Other studies focused on prioritising/triaging reports, detecting follow-up recommendations, and linking a follow-up exam to the initial recommendation report, or bio-surveillance of infectious conditions, such as invasive mould disease.…”

Section: Diagnostic Surveillancementioning

confidence: 99%

A systematic review of natural language processing applied to radiology reports

Casey

Davidson

Poon

et al. 2021

BMC Med Inform Decis Mak

100

View full text Add to dashboard Cite

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.

show abstract

Section: Diagnostic Surveillancementioning

confidence: 99%

A systematic review of natural language processing applied to radiology reports

Casey

Davidson

Poon

et al. 2021

BMC Med Inform Decis Mak

100

View full text Add to dashboard Cite

show abstract

“…Studies in this category also investigated incidental findings including on lung imaging [Farjah et al, 2016, Karunakaran et al, 2017, Tan et al, 2018, with [Farjah et al, 2016] additionally extracting the nodule size; for trauma patients [Trivedi et al, 2019]; and looking for silent brain infarction and white matter disease [Fu et al, 2019]. Other studies focused on prioritising/triaging reports, detecting follow-up recommendations, and linking a follow-up exam to the initial recommendation report, or bio-surveillance of infectious conditions, such as invasive mould disease.…”

Section: Clinical Application Categoriesmentioning

confidence: 99%

A Systematic Review of Natural Language Processing Applied to Radiology Reports

Casey,

Davidson,

Poon

et al. 2021

Preprint

View full text Add to dashboard Cite

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 4,799 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

show abstract

“…We therefore opted to use a simple shallow convolutional neural network (CNN), which is classically used for feature detection tasks and has been used for text classification tasks in the past, 22 including a 2019 study on identifying incidental findings in radiology reports. 23 Our decision to use a convolutional, rather than recurrent neural network (RNN), for text classification also relates to the efficiency of these networks for the stated feature detection task. Less computationally intensive than RNNs, CNNs can run effectively with limited resources.…”

Section: Neural Network Design and Training Protocolmentioning

confidence: 99%

“…Documents were tokenized using the built-in SpaCy tokenizer. 26 Although more task-specific word embeddings exist, 23 our network was trained with GloVe word embeddings to improve model robustness and ensure similar model performance across institutions. ►Appendix A.1 contains further discussion on the decision to use GloVe word embeddings.…”

Section: Neural Network Design and Training Protocolmentioning

confidence: 99%

A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning

et al. 2020

View full text Add to dashboard Cite

Background Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due to the lack of tools for the management of such findings and the time required to maintain up-to-date lists. Natural language processing (NLP) is capable of extracting information from free-text clinical documents and could provide the basis for software solutions that do not require changes to clinical workflows. Objectives In this manuscript we present (1) a machine learning algorithm we trained to identify radiology reports documenting the presence of a newly discovered adrenal incidentaloma, and (2) the web application and results database we developed to manage these clinical findings. Methods We manually annotated a training corpus of 4,090 radiology reports from across our institution with a binary label indicating whether or not a report contains a newly discovered adrenal incidentaloma. We trained a convolutional neural network to perform this text classification task. Over the NLP backbone we built a web application that allows users to coordinate clinical management of adrenal incidentalomas in real time. Results The annotated dataset included 404 positive (9.9%) and 3,686 (90.1%) negative reports. Our model achieved a sensitivity of 92.9% (95% confidence interval: 80.9–97.5%), a positive predictive value of 83.0% (69.9–91.1)%, a specificity of 97.8% (95.8–98.9)%, and an F1 score of 87.6%. We developed a front-end web application based on the model's output. Conclusion Developing an NLP-enabled custom web application for tracking and management of high-risk adrenal incidentalomas is feasible in a resource constrained, safety net hospital. Such applications can be used by an institution's quality department or its primary care providers and can easily be generalized to other types of clinical findings.

show abstract

Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods

Cited by 12 publications

References 22 publications

A systematic review of natural language processing applied to radiology reports

A systematic review of natural language processing applied to radiology reports

A Systematic Review of Natural Language Processing Applied to Radiology Reports

A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning

Contact Info

Product

Resources

About