Extracting clinical terms from radiology reports with deep learning

Sugimoto, Kento; Takeda, Toshihiro; Oh, Jong–Hoon; Wada, Shoya; Konishi, Shozo; Yamahata, Asuka; Manabe, Shiro; Tomiyama, Noriyuki; Matsunaga, Takashi; Nakanishi, Katsuyuki; Matsumura, Yasushi

doi:10.1016/j.jbi.2021.103729

Cited by 30 publications

(30 citation statements)

References 24 publications

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“…When the data is textual, the extraction of features is a bit different where the aim is to create word or text embeddings. Generally in medical NLP and at the feature extraction level, the BERT (as a state-of-the-art model) was used in several studies as in [14] , [15] , [16] , [17] , whereas, the Word2Vec model was used in [18] , [19] , [20] . In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In contrast, and at the algorithmic level, various deep learning models were widely used in medical NLP. Such as: The BiLSTM/LSTM in [14] , [16] , [20] , [21] , [22] , [23] , [24] , the convolutional neural network (CNN) in [19] , [25] , the capsule network [26] , the transformers [27] , the ResNet-34 network [28] , and the generative adversarial network (GAN) [29] . Medical symptom extraction is a well-known problem in health or medical-related NLP tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Faris

Faris²,

Habib³

et al. 2022

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Faris

Faris²,

Habib³

et al. 2022

Heliyon

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“…While analysis tools [12][13][14] have been developed to extract key clinical concepts and their attributes from biomedical text and convert them into a structured format, such dictionary-and rule-based annotation systems are often limited in their report coverage and generalizability across institutions. Another approach aims to capture more specific and detailed information from radiology reports by adopting entity extraction schemas [8,11] or schemas that focus on facts [10] and spatial relations [9]. A central limitation of both of these approaches is that they require task-specific datasets to be densely annotated by domain experts.…”

Section: Related Workmentioning

confidence: 99%

“…Large-scale datasets, such as MIMIC-CXR [1] and CheXpert [2], use automated radiology report labelers [2][3][4][5][6] to extract common medical conditions from reports. Other approaches [7][8][9][10][11] aim to extract more fine-grained information in reports. The development of automated approaches for structuring large amounts of clinically relevant information in reports is primarily limited by two factors.…”

Section: Introductionmentioning

confidence: 99%

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Jain,

Agrawal,

Saporta

et al. 2021

Preprint

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Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on a novel information extraction schema we designed to structure radiology reports. We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579 entities and 10,889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets. Using these datasets, we train and test a deep learning model, RadGraph Benchmark, that achieves a micro F1 of 0.82 and 0.73 on relation extraction on the MIMIC-CXR and CheXpert test sets respectively. Additionally, we release an inference dataset, which contains annotations automatically generated by RadGraph Benchmark across 220,763 MIMIC-CXR reports (around 6 million entities and 4 million relations) and 500 CheXpert reports (13,783 entities and 9,908 relations) with mappings to associated chest radiographs. Our freely available dataset can facilitate a wide range of research in medical natural language processing, as well as computer vision and multi-modal learning when linked to chest radiographs. * Equal Contribution † Equal Contribution Preprint. Under review.

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“…Natural language processing (NLP) offers the opportunity to automatically extract information to support the application of predictive models [ 17 , 22 ]. Many studies used rule-based, machine learning, or deep learning methods to extract the cancer-related information from free-text EMR data [ 22 - 29 ], but only a few included further elaboration on how to exploit the extracted information. Chen et al [ 30 ] extracted information from various clinical notes including CT reports and operative notes to calculate the Cancer of the Liver Italian Program score.…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

Hu¹,

Li²,

Zhang³

et al. 2022

JMIR Med Inform

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Background Lymph node metastasis (LNM) is critical for treatment decision making of patients with resectable non–small cell lung cancer, but it is difficult to precisely diagnose preoperatively. Electronic medical records (EMRs) contain a large volume of valuable information about LNM, but some key information is recorded in free text, which hinders its secondary use. Objective This study aims to develop LNM prediction models based on EMRs using natural language processing (NLP) and machine learning algorithms. Methods We developed a multiturn question answering NLP model to extract features about the primary tumor and lymph nodes from computed tomography (CT) reports. We then combined these features with other structured clinical characteristics to develop LNM prediction models using machine learning algorithms. We conducted extensive experiments to explore the effectiveness of the predictive models and compared them with size criteria based on CT image findings (the maximum short axis diameter of lymph node >10 mm was regarded as a metastatic node) and clinician’s evaluation. Since the NLP model may extract features with mistakes, we also calculated the concordance correlation between the predicted probabilities of models using NLP-extracted features and gold standard features to explore the influence of NLP-driven automatic extraction. Results Experimental results show that the random forest models achieved the best performances with 0.792 area under the receiver operating characteristic curve (AUC) value and 0.456 average precision (AP) value for pN2 LNM prediction and 0.768 AUC value and 0.524 AP value for pN1&N2 LNM prediction. And all machine learning models outperformed the size criteria and clinician’s evaluation. The concordance correlation between the random forest models using NLP-extracted features and gold standard features is 0.950 and improved to 0.984 when the top 5 important NLP-extracted features were replaced with gold standard features. Conclusions The LNM models developed can achieve competitive performance using only limited EMR data such as CT reports and tumor markers in comparison with the clinician’s evaluation. The multiturn question answering NLP model can extract features effectively to support the development of LNM prediction models, which may facilitate the clinical application of predictive models.

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Extracting clinical terms from radiology reports with deep learning

Cited by 30 publications

References 24 publications

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non–Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study

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