Clinically Significant Information Extraction from Radiology Reports

Nandhakumar, Nidhin; Sherkat, Ehsan; Milios, Evangelos; Gu, Hong; Butler, Michael J.

doi:10.1145/3103010.3103023

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Existing approaches to overcome the lack of labelled data include using a rule-based system to annotate more data (Smit et al, 2020) or propose labels in an annotation tool (Nandhakumar et al, 2017;Searle et al, 2019), leveraging semi-supervised learning to speed up annotation (Wood et al, 2020) and creating artificial data (Schrempf et al, 2020). It is also common for rule-based systems to be developed alongside statistical models to contrast their performance (Cornegruta et al, 2016;Gorinski et al, 2019;Sykes et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Grivas¹,

Alex²,

Grover³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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We present an in-depth comparison of three clinical information extraction (IE) systems designed to perform entity recognition and negation detection on brain imaging reports: EdIE-R, a bespoke rule-based system, and two neural network models, EdIE-BiLSTM and EdIE-BERT, both multi-task learning models with a BiLSTM and BERT encoder respectively. We compare our models both on an in-sample and an out-of-sample dataset containing mentions of stroke findings and draw on our error analysis to suggest improvements for effective annotation when building clinical NLP models for a new domain. Our analysis finds that our rulebased system outperforms the neural models on both datasets and seems to generalise to the out-of-sample dataset. On the other hand, the neural models do not generalise negation to the out-of-sample dataset, despite metrics on the in-sample dataset suggesting otherwise.

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Section: Introductionmentioning

confidence: 99%

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Grivas¹,

Alex²,

Grover³

et al. 2020

Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

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“…For text, Wu et al used natural language analysis to automatically extract keyword lists from pathological examination reports [33]. Nandhakumar et al used the characteristics of words or sentences and conditional random field (CRF) models to extract important parts of medical reports [34]. The methods described above are attempts made to structure data.…”

Section: Machine-learning-based Methods For Construction Data Processingmentioning

confidence: 99%

ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example

Tsai

2021

Sustainability

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This study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data management system full of data in an inconsistent format or even incorrect data. Processing the construction data into a machine-readable format is not only time-consuming but also labor-intensive. Therefore, this study used Taiwan’s public construction data as an example case to develop a natural language processing (NLP) and machine learning-based text classification system, coined as automatic correction system (ACS). The developed system is designed to automatically correct the public construction data, meanwhile improving the efficiency of manual data correction. The ACS has two main features: data correction that converts unstructured data into structured data; a recommendation function that provides users with a recommendation list for manual data correction. For implementation, the developed system was used to correct the data in the public construction cost estimation system (PCCES) in Taiwan. We expect that the ACS can improve the accuracy of the data in the public construction database to increase the efficiency of the practitioners in executing projects. The results show that the system can correct 18,511 data points with an accuracy of 76%. Additionally, the system was also validated to reduce the system operation time by 51.69%.

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“…Extracting clinical findings from clinical reports has been explored previously (Hassanpour and Langlotz, 2016;Nandhakumar et al, 2017). For summarizing radiology reports, Zhang et al (2018) recently used a separate RNN to encode a section of radiology report.…”

Section: Related Workmentioning

confidence: 99%

Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization

Gharebagh¹,

Goharian²,

Filice³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Sequence-to-sequence (seq2seq) network is a well-established model for text summarization task. It can learn to produce readable content; however, it falls short in effectively identifying key regions of the source. In this paper, we approach the content selection problem for clinical abstractive summarization by augmenting salient ontological terms into the summarizer. Our experiments on two publicly available clinical data sets (107,372 reports of MIMIC-CXR, and 3,366 reports of OpenI) show that our model statistically significantly boosts state-of-the-art results in terms of ROUGE metrics (with improvements: 2.9% RG-1, 2.5% RG-2, 1.9% RG-L), in the healthcare domain where any range of improvement impacts patients' welfare.

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Clinically Significant Information Extraction from Radiology Reports

Cited by 6 publications

References 19 publications

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

Not a cute stroke: Analysis of Rule- and Neural Network-based Information Extraction Systems for Brain Radiology Reports

ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example

Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization

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