The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study

Olthof, A; Ooijen, Peter M. A. van; Cornelissen, Ludo J.

doi:10.1177/14604582221131198

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Similarly, the BioBERT model that was trained on the same input text as BERT supplemented with PubMed abstracts and full-text articles significantly outperforms BERT on biomedical named entity recognition, question answering and relation extraction [ 19 ]. Within the existing literature in the clinical domain, domain-specific models are shown to outperform fine-tuned general BERT models virtually every time a direct comparison takes place, such as for the aforementioned ClinicalBERT [ 11 , 16 , 18 , 20 – 22 ] and BioBERT [ 11 , 19 , 21 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Extracting patient lifestyle characteristics from Dutch clinical text with BERT models

Muizelaar,

Haas,

van Dortmont

et al. 2024

BMC Med Inform Decis Mak

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Background BERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted into classifying unstructured clinical notes on the basis of patient lifestyle indicators, especially in Dutch. This article aims to test the feasibility of deep BERT models on the task of patient lifestyle classification, as well as introducing an experimental framework that is easily reproducible in future research. Methods This study makes use of unstructured general patient text data from HagaZiekenhuis, a large hospital in The Netherlands. Over 148 000 notes were provided to us, which were each automatically labelled on the basis of the respective patients’ smoking, alcohol usage and drug usage statuses. In this paper we test feasibility of automatically assigning labels, and justify it using hand-labelled input. Ultimately, we compare macro F1-scores of string matching, SGD and several BERT models on the task of classifying smoking, alcohol and drug usage. We test Dutch BERT models and English models with translated input. Results We find that our further pre-trained MedRoBERTa.nl-HAGA model outperformed every other model on smoking (0.93) and drug usage (0.77). Interestingly, our ClinicalBERT model that was merely fine-tuned on translated text performed best on the alcohol task (0.80). In t-SNE visualisations, we show our MedRoBERTa.nl-HAGA model is the best model to differentiate between classes in the embedding space, explaining its superior classification performance. Conclusions We suggest MedRoBERTa.nl-HAGA to be used as a baseline in future research on Dutch free text patient lifestyle classification. We furthermore strongly suggest further exploring the application of translation to input text in non-English clinical BERT research, as we only translated a subset of the full set and yet achieved very promising results.

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Section: Introductionmentioning

confidence: 99%

Extracting patient lifestyle characteristics from Dutch clinical text with BERT models

Muizelaar,

Haas,

van Dortmont

et al. 2024

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Similarly, the BioBERT model that was trained on the same input text as BERT supplemented with PubMed abstracts and full-text articles significantly outperforms BERT on biomedical named entity recognition, question answering and relation extraction [19]. Within the existing literature in the clinical domain, domain-specific models are shown to outperform fine-tuned general BERT models virtually every time a direct comparison takes place, such as for the aforementioned ClinicalBERT [11,16,18,[20][21][22] and BioBERT [11,19,21,23,24].…”

Section: Introductionmentioning

confidence: 94%

“…A popular improvement suggestion, the so-called Robustly Optimized BERT pre-training Approach (RoBERTa) model, makes use of a more optimized set of hyperparameters and a more dynamic pre-training task [7]. The application of the RoBERTa architecture has shown to outperform the original BERT model in direct comparisons on a relatively large amount of tasks [7][8][9][10][11][12][13][14][15][16][17]. Over the years, further adjustments have been made to the RoBERTa architecture.…”

Section: Introductionmentioning

confidence: 99%

Extracting Patient Lifestyle Characteristics from Dutch Clinical Text with BERT Models

Muizelaar,

Haas,

Dortmont

et al. 2024

Preprint

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Background: BERT models have seen widespread use on unstructured text within the clinical domain. However, little to no research has been conducted into classifying unstructured clinical notes on the basis of patient lifestyle indicators, especially in Dutch. This article aims to test the feasibility of deep BERT models on the task of patient lifestyle classification, as well as introducing an experimental framework that is easily reproducible in future research. Methods: This study makes use of unstructured general patient text data from HagaZiekenhuis, a large hospital in The Netherlands. Over 148 000 notes were provided to us, which were each automatically labelled on the basis of the respective patients’ smoking, alcohol usage and drug usage statuses. In this paper we test feasibility of automatically assigning labels, and justify it using hand-labelled input. Ultimately, we compare macro F1-scores of string matching, SGD and several BERT models on the task of classifying smoking, alcohol and drug usage. We test Dutch BERT models and English models with translated input. Results: We find that our further pre-trained MedRoBERTa.nl-HAGA model outperformed every other model on smoking (0.93) and drug usage (0.77). Interestingly, our ClinicalBERT model that was merely fine-tuned on translated text performed best on the alcohol task (0.80). In t-SNE visualisations, we show our MedRoBERTa.nl-HAGA model is the best model to differentiate between classes in the embedding space, explaining its superior classification performance. Conclusions: We suggest MedRoBERTa.nl-HAGA to be used as a baseline in future research on Dutch free text patient lifestyle classification. We furthermore strongly suggest further exploring the application of translation to input text in non-English clinical BERT research, as we only translated a subset of the full set and yet achieved very promising results.

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“…Similar tendencies can be observed in the medical domain. The language-specific Dutch model, RobBERT, outperformed multilingual BERT (mBERT) on the multilabel classification of chest imaging requests and report items [51]. The Swedish KB-BERT model outperformed mBERT when fine-tuned for the de-identification task, albeit marginally [52].…”

Section: Pretrained Language Models For Loementioning

confidence: 99%

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Shaitarova,

Zaghir,

Lavelli

et al. 2023

Yearb Med Inform

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Objectives: This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods: We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results: Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.

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The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study

Cited by 5 publications

References 43 publications

Extracting patient lifestyle characteristics from Dutch clinical text with BERT models

Extracting patient lifestyle characteristics from Dutch clinical text with BERT models

Extracting Patient Lifestyle Characteristics from Dutch Clinical Text with BERT Models

Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

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