Adapting and evaluating a deep learning language model for clinical why-question answering

Wen, Andrew; Elwazir, Mohamed Y.; Moon, Sungrim; Fan, Jungwei

doi:10.1093/jamiaopen/ooz072

Cited by 22 publications

(14 citation statements)

References 13 publications

(13 reference statements)

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“…Transformer-based models have been wildly successful in setting state-of-the-art benchmarks on a broad range of natural language processing (NLP) tasks, including question answering, document classification, machine translation, text summarization, and others [1][2][3]. These successes have been replicated in the clinical and biomedical domain via pre-training language models using large-scale clinical or biomedical corpora, then fine-tuning on a variety of clinical or biomedical downstream tasks, including computational phenotyping [4], automatical ICD coding [5], knowledge graph completion [6] and clinical question answering [7].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of pretrained language models for long clinical text

Wehbe

Ahmad

et al. 2022

Journal of the American Medical Informatics Association

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Objective Clinical knowledge-enriched transformer models (eg, ClinicalBERT) have state-of-the-art results on clinical natural language processing (NLP) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (eg, Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts. Materials and methods Inspired by the success of long-sequence transformer models and the fact that clinical notes are mostly long, we introduce 2 domain-enriched language models, Clinical-Longformer and Clinical-BigBird, which are pretrained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks. Results The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results. Discussion Our pretrained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pretrained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer. Conclusion This study demonstrates that clinical knowledge-enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers.

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Section: Introductionmentioning

confidence: 99%

A comparative study of pretrained language models for long clinical text

Wehbe

Ahmad

et al. 2022

Journal of the American Medical Informatics Association

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“…The size of the token dictionary used in the test used approximately 30,000 sub tokens in both the SentencePiece and ByteLevelBPE methods. The test was carried out with the learning rates of 1e-5 and 5e-5 for both languages according to the reference from the BERT paper [17], [22], [26].…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we proposed RoBERTa − an artificial neural network based on transformers with 6 layers (base model). RoBERTa [22], [23] used the WordPiece tokenization technique in the pre-training stage and the training method used masked layer modelling (MLM) and NSP to support our QAS system. Our contribution are: 1) representing the extraction of language models at the character level for answer selection without any engineering features and linguistic tools; and 2) applying an efficient self-attention model to generate answers according to context by calculating the input and output representations regardless of word order.…”

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confidence: 99%

RoBERTa: language modelling in building Indonesian question-answering systems

et al. 2022

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This research aimed to evaluate the performance of the A Lite BERT (ALBERT), efficiently learning an encoder that classifies token replacements accurately (ELECTRA) and a robust optimized BERT pretraining approach (RoBERTa) models to support the development of the Indonesian language question and answer system model. The evaluation carried out used Indonesian, Malay and Esperanto. Here, Esperanto was used as a comparison of Indonesian because it is international, which does not belong to any person or country and this then make it neutral. Compared to other foreign languages, the structure and construction of Esperanto is relatively simple. The dataset used was the result of crawling Wikipedia for Indonesian and Open Super-large Crawled ALMAnaCH coRpus (OSCAR) for Esperanto. The size of the token dictionary used in the test used approximately 30,000 sub tokens in both the SentencePiece and byte-level byte pair encoding methods (ByteLevelBPE). The test was carried out with the learning rates of 1e-5 and 5e-5 for both languages in accordance with the reference from the bidirectional encoder representations from transformers (BERT) paper. As shown in the final result of this study, the ALBERT and RoBERTa models in Esperanto showed the results of the loss calculation that were not much different. This showed that the RoBERTa model was better to implement an Indonesian question and answer system.

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“…In this study, we focus on clinical reading comprehension task, which aims to extract a text span (a sentence or multiple sentences) as the answer from a patient clinical note given a question (Yue et al, 2020). Though many neural models (Seo et al, 2017;Rawat et al, 2020;Wen et al, 2020) have achieved impressive results on this task, their performance on new clinical contexts, whose data distributions could be different from the ones that these models were trained on, is still far from satisfactory (Yue et al, 2020). Though one can improve the performance by adding more QA pairs on new contexts into training, however, manually creating large-scale QA pairs in the clinical domain often involves tremendous expert effort and data privacy concerns.…”

Section: Related Workmentioning

confidence: 99%

CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

Yue¹,

Zhang²,

Yao³

et al. 2020

Preprint

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Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where large-scale QA pairs are not readily available for retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order to generate diverse types of questions that are essential for training QA models, we further introduce a seq2seq-based question phrase prediction (QPP) module that can be used together with most existing QG models to diversify their generation. Our comprehensive experiment results show that the QA corpus generated by our framework is helpful to improve QA models on the new contexts (up to 8% absolute gain in terms of Exact Match), and that the QPP module plays a crucial role in achieving the gain. 1

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Adapting and evaluating a deep learning language model for clinical why-question answering

Cited by 22 publications

References 13 publications

A comparative study of pretrained language models for long clinical text

A comparative study of pretrained language models for long clinical text

RoBERTa: language modelling in building Indonesian question-answering systems

CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering

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