Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models (original) (raw)
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arXiv (Cornell University), 2023
Clinical texts and documents contain a wealth of information and knowledge in the field of healthcare, and their processing, using state-of-the-art language technology, has become very important for building intelligent systems capable of supporting healthcare and providing greater social good. This processing includes creating language understanding models and translating resources into other natural languages to share domain-specific cross-lingual knowledge. In this work, we conduct investigations on clinical text machine translation by examining multilingual neural network models using deep learning methods such as Transformer-based structures. Furthermore, to address the issue of language resource imbalance, we also carry out experiments using a transfer learning methodology based on massive multilingual pre-trained language models (MMPLMs). The experimental results on three sub-tasks including 1) clinical case (CC), 2) clinical terminology (CT), and 3) ontological concept (OC) show that our models achieved toplevel performances in the ClinSpEn-2022 shared task on English-Spanish clinical domain data. Furthermore, our expert-based human evaluations demonstrate that the small-sized pre-trained language model (PLM) wins in the clinical domain fine-tuning over the other two extra-large language models by a large margin. This finding has never been previously reported in the field. Finally, the transfer learning method works well in our experimental setting using the WMT21fb model to accommodate a new Spanish language space that was not seen at the pretraining stage within WMT21fb itself-and this deserves further exploration for clinical knowledge transformation, e.g. investigation into more languages. These research findings can shed some light on domain-specific machine translation development, especially in clinical and healthcare fields. Further research projects can be carried out based on our work to improve healthcare text analytics and knowledge transformations. Our data will be openly available for research purposes at
2021
This work presents biomedical and clinical language models for Spanish by experimenting with different pretraining choices, such as masking at word and subword level, varying the vocabulary size and testing with domain data, looking for better language representations. Interestingly, in the absence of enough clinical data to train a model from scratch, we applied mixed-domain pretraining and cross-domain transfer approaches to generate a performant bio-clinical model suitable for real-world clinical data. We evaluated our models on Named Entity Recognition (NER) tasks for biomedical documents and challenging hospital discharge reports. When compared against the competitive mBERT and BETO models, we outperform them in all NER tasks by a significant margin. Finally, we studied the impact of the model’s vocabulary on the NER performances by offering an interesting vocabulary-centric analysis. The results confirm that domain-specific pretraining is fundamental to achieving higher perfor...
Neural machine translation of clinical texts between long distance languages
Journal of the American Medical Informatics Association, 2019
Objective To analyze techniques for machine translation of electronic health records (EHRs) between long distance languages, using Basque and Spanish as a reference. We studied distinct configurations of neural machine translation systems and used different methods to overcome the lack of a bilingual corpus of clinical texts or health records in Basque and Spanish. Materials and Methods We trained recurrent neural networks on an out-of-domain corpus with different hyperparameter values. Subsequently, we used the optimal configuration to evaluate machine translation of EHR templates between Basque and Spanish, using manual translations of the Basque templates into Spanish as a standard. We successively added to the training corpus clinical resources, including a Spanish-Basque dictionary derived from resources built for the machine translation of the Spanish edition of SNOMED CT into Basque, artificial sentences in Spanish and Basque derived from frequently occurring relationships in...
Proceedings of the 5th Clinical Natural Language Processing Workshop
Massively multilingual pre-trained language models (MMPLMs) are developed in recent years demonstrating superpowers and the pre-knowledge they acquire for downstream tasks. This work investigates whether MMPLMs can be applied to clinical domain machine translation (MT) towards entirely unseen languages via transfer learning. We carry out an experimental investigation using Meta-AI's MMPLMs "wmt21-dense-24-wide-en-X and X-en (WMT21fb)" which were pre-trained on 7 language pairs and 14 translation directions including English to Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese, and the opposite direction. We fine-tune these MMPLMs towards English-Spanish language pair which did not exist at all in their original pre-trained corpora both implicitly and explicitly. We prepare carefully aligned clinical domain data for this fine-tuning, which is different from their original mixed domain knowledge. Our experimental result shows that the fine-tuning is very successful using just 250k well-aligned in-domain EN-ES segments for three sub-task translation testings: clinical cases, clinical terms, and ontology concepts. It achieves very close evaluation scores to another MMPLM NLLB from Meta-AI, which included Spanish as a high-resource setting in the pre-training. To the best of our knowledge, this is the first work on using MMPLMs towards clinical domain transferlearning NMT successfully for totally unseen languages during pre-training.
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling
Cornell University - arXiv, 2022
Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA. However, the applicability of these methods to real clinical use cases is hindered, due to the limitation of pre-trained LMs in processing long textual data with thousands of words, which is a common length for a clinical note. In this work, we explore long-range adaptation from such LMs with Longformer, allowing the LMs to capture longer clinical notes context. We conduct experiments on three n2c2 challenges datasets and a longitudinal clinical dataset from Hong Kong Hospital Authority electronic health record (EHR) system to show the effectiveness and generalizability of this concept, achieving 10% F1-score improvement. Based on our experiments, we conclude that capturing a longer clinical note interval is beneficial to the model performance, but there are different cutoff intervals to achieve the optimal performance for different target variables.
Cornell University - arXiv, 2022
Massively multilingual pre-trained language models (MMPLMs) are developed in recent years demonstrating super powers and the preknowledge they acquire for downstream tasks. In this work, we investigate whether MM-PLMs can be applied to zero-shot machine translation (MT) towards entirely new language pairs and new domains. We carry out experimental investigation using Meta-AI's MM-PLMs "wmt21-dense-24-wide-en-X and X-en (WMT21fb)" which were pre-trained on 7 language pairs and 14 translation directions including English to Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese, and opposite direction. We fine-tune these MM-PLMs towards English-Spanish language pair which did not exist at all in their original pre-trained corpora both implicitly and explicitly. We prepare carefully aligned clinical domain data for this fine-tuning, which is different from their original mixed domain knowledge as well. Our experimental result shows that the fine-tuning is very successful using just 250k well-aligned in-domain EN-ES pairs / sentences for three sub-task translation testings: clinical cases, clinical terms and ontology concepts. It achieves very close evaluation scores to another MMPLM NLLB from Meta-AI, which included Spanish as a highresource setting in the pre-training. To the best of our knowledge, this is the first work on using MMPLMs towards real zero-shot NMT successfully for totally unseen languages during pre-training, and also the first in clinical domain for such study.
Spanish Biomedical Crawled Corpus: A Large, Diverse Dataset for Spanish Biomedical Language Models
ArXiv, 2021
We introduce CoWeSe (the Corpus Web Salud Español), the largest Spanish biomedical corpus to date, consisting of 4.5GB (about 750M tokens) of clean plain text. CoWeSe is the result of a massive crawler on 3000 Spanish domains executed in 2020. The corpus is openly available and already preprocessed. CoWeSe is an important resource for biomedical and health NLP in Spanish and has already been employed to train domain-specific language models and to produce word embbedings. We released the CoWeSe corpus under a Creative Commons Attribution 4.0 International license in Zenodo (https://doi.org/10.5281/zenodo.4561970).
Enriching Biomedical Knowledge for Low-resource Language Through Large-Scale Translation
arXiv (Cornell University), 2022
Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English, such as Vietnamese. In this paper, we use a state-of-theart translation model in English-Vietnamese to translate and produce both pretrained and supervised data in the biomedical domains. Thanks to such large-scale translation, we introduce ViPubmedT5, a pretrained Encoder-Decoder Transformer model trained on 20 million translated abstracts from the high-quality public PubMed corpus. ViPubMedT5 demonstrates state-of-the-art results on two different biomedical benchmarks in summarization and acronym disambiguation. Further, we release ViMedNLI-a new NLP task in Vietnamese translated from MedNLI using the recently public En-vi translation model and carefully refined by human experts, with evaluations of existing methods against ViPubmedT5.
The introduction of computerized medical records in hospitals has reduced burdensome operations like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting them from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation, using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Large Language Model for this task. Moreover, we conducted several experiments with three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a "few-shot" approach. This allowed us to establish methodological guidelines that pave the way for future implementations in this field and allow Italian hospitals to tap into important research opportunities.
A large language model for electronic health records
npj digital medicine, 2022
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery.