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Healthcare NER Models Using Language Model Pretraining [article]

Amogh Kamat Tarcar, Aashis Tiwari, Vineet Naique Dhaimodker, Penjo Rebelo, Rahul Desai, Dattaraj Rao
2020 arXiv   pre-print
Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER) [1] model trained  ...  the results presented is that the F1 score of model (0.734) trained with our approach with just 50% of available training data outperforms the F1 score of the blank spaCy model without language model  ...  We developed custom healthcare NER models to extract phrases related to (pharmaceutical) chemicals with dosage, diseases and symptoms from EHRs.  ... 
arXiv:1910.11241v2 fatcat:vfwj3welx5br5d47jpn4u2ggt4

BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model [article]

Hongyi Yuan, Zheng Yuan, Ruyi Gan, Jiaxing Zhang, Yutao Xie, Sheng Yu
2022 arXiv   pre-print
Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks.  ...  In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain.  ...  They pretrain their models with a mixture of masked language modeling and auto-regressive language generation.  ... 
arXiv:2204.03905v2 fatcat:sdczfns265ezplidcz5mcebkee

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos [article]

Dianbo Liu, Tim Miller
2020 arXiv   pre-print
In this article, we show that it is possible to both pretrain and fine tune BERT models in a federated manner using clinical texts from different silos without moving the data.  ...  Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years.  ...  Firstly of all, due to limit of data access, we used clinical notes from a single healthcare system to simulate different silos.  ... 
arXiv:2002.08562v1 fatcat:n6leku5xdvcenohyik6j4xchqq

On Biomedical Named Entity Recognition: Experiments in Interlingual Transfer for Clinical and Social Media Texts [chapter]

Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina
2020 Lecture Notes in Computer Science  
In this work, we present a fine-grained evaluation intended to understand the efficiency of multilingual BERT-based models for bioNER of drug and disease mentions across two domains in two languages, namely  ...  TL reduces the amount of labeled data needed to achieve high performance on three out of four corpora: pretrained models reach 98-99% of the full dataset performance on both types of entities after training  ...  NER models in the biomedical field.  ... 
doi:10.1007/978-3-030-45442-5_35 fatcat:q6kplymurfbnbimgf5iejkdevi

Spanish Pre-Trained Language Models for HealthCare Industry

Jalaj Harkawat, Tejas Vaidhya
2021 Annual Conference of the Spanish Society for Natural Language Processing  
But the terminologies used in Healthcare sector such as names of different diseases, medicines and departments makes it difficult to predict with high accuracy.  ...  Experimental results have shown that our model gives better results than the current baseline of MEDDOPROF Shared task.  ...  on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective.  ... 
dblp:conf/sepln/HarkawatV21 fatcat:jrpdoo2pwrcezptaigmqic4ili

Iterative Annotation of Biomedical NER Corpora with Deep Neural Networks and Knowledge Bases

Stefano Silvestri, Francesco Gargiulo, Mario Ciampi
2022 Applied Sciences  
The obtained corpus was used to train a B-NER deep neural network whose performances are comparable with the state of the art, with an F1-Score equal to 0.9661 and 0.8875 on two test sets.  ...  For these reasons, healthcare professionals lose big opportunities that can arise from the analysis of this data.  ...  The approach was tested by creating an Italian language B-NER corpus used to train different B-NER DNNs.  ... 
doi:10.3390/app12125775 fatcat:oby2lpz52vbmlbfwdifk4cuqye

Spark NLP: Natural Language Understanding at Scale [article]

Veysel Kocaman, David Talby
2021 arXiv   pre-print
Spark NLP comes with 1100 pre trained pipelines and models in more than 192 languages. It supports nearly all the NLP tasks and modules that can be used seamlessly in a cluster.  ...  Downloaded more than 2.7 million times and experiencing nine times growth since January 2020, Spark NLP is used by 54% of healthcare organizations as the worlds most widely used NLP library in the enterprise  ...  Common use cases include question answering, paraphrasing or summarising, sentiment analysis, natural language BI, language modelling, and disambiguation.  ... 
arXiv:2101.10848v1 fatcat:niua3vh3ujcwtge3m47e5entva

GERNERMED – An Open German Medical NER Model [article]

Johann Frei, Frank Kramer
2021 arXiv   pre-print
that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model.  ...  In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER).  ...  of sequential text data using pretrained models.  ... 
arXiv:2109.12104v2 fatcat:wcaq5wlfdvcafpm2afdkk72tla

TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla [article]

Nazia Tasnim, Md. Istiak Hossain Shihab, Asif Shahriyar Sushmit, Steven Bethard, Farig Sadeque
2022 arXiv   pre-print
We have leveraged the ensemble of multiple ELECTRA-based models that were exclusively pretrained on the Bangla language with the performance of ELECTRA-based models pretrained on English to achieve competitive  ...  Many areas, such as the biological and healthcare domain, artistic works, and organization names, have nested, overlapping, discontinuous entity mentions that may even be syntactically or semantically  ...  models in different NER benchmark datasets.  ... 
arXiv:2204.09964v1 fatcat:cqstetgg25ci3ifykynwjycc3e

Improving Clinical Document Understanding on COVID-19 Research with Spark NLP [article]

Veysel Kocaman, David Talby
2020 arXiv   pre-print
Third, the deep learning models used are more accurate than previously available, leveraging an integrated pipeline of state-of-the-art pretrained named entity recognition models, and improving on the  ...  the ability to train models to support new entity types or human languages with no code changes.  ...  Spark NLP currently supports 46 languages and 3 languages for Healthcare -English, German and Spanish.  ... 
arXiv:2012.04005v1 fatcat:rjyeeeewyzabjm333vfsx6lmb4

Spark NLP: Natural language understanding at scale

Veysel Kocaman, David Talby
2021 Software Impacts  
We also thank our users and customers who helped us improve the library with their feedbacks and suggestions.  ...  There are more than 40 pretrained NER models in Spark NLP Enterprise edition.  ...  Common use cases include question answering, paraphrasing or summarizing, sentiment analysis, natural language BI, language modeling, and disambiguation.  ... 
doi:10.1016/j.simpa.2021.100058 fatcat:qvt7zav7zrgxdob3u6hchid53i

CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

Sicheng Zhou, Nan Wang, Liwei Wang, Hongfang Liu, Rui Zhang
2022 JAMIA Journal of the American Medical Informatics Association  
We kept pretraining the BlueBERT model on the cancer corpus with expanded vocabularies (using both term frequency-based and manually reviewed methods) to obtain CancerBERT models.  ...  Results All CancerBERT models outperformed all other models on the cancer phenotyping NER task.  ...  23 models on the NER task.  ... 
doi:10.1093/jamia/ocac040 pmid:35333345 pmcid:PMC9196678 fatcat:mj5yz3ojknaahjxfyd257kdfn4

Multilingual Medical Question Answering and Information Retrieval for Rural Health Intelligence Access [article]

Vishal Vinod, Susmit Agrawal, Vipul Gaurav, Pallavi R, Savita Choudhary
2021 arXiv   pre-print
Using the input from subject matter experts, we have compiled a large corpus to pre-train and fine-tune our BioBERT based NLP model for the specific tasks.  ...  In this paper, we describe an approach leveraging the phenomenal progress in Machine Learning and NLP (Natural Language Processing) techniques to design a model that is low-resource, multilingual, and  ...  ., 2018) masked language model (MLM) has become one of the popular NLP architectures for NER (Named Entity Recognition), relation extraction, and question-answering.  ... 
arXiv:2106.01251v1 fatcat:hmbqc26khvexrffcechbqlitcq

Feature Extraction Method from Electronic Health Records in Russia

Alexander Gusev, Igor Korsakov, Roman Novitsky, Larisa Serova, Denis Gavrilov
2020 Zenodo  
We present a method of using statistical NER parsers on a medical corpus of Russian. We developed a new tool that gives a convenient way to extract NER from unstructured medical documents  ...  At the same time, the use of NER parsing in NLP applications has increased. It can be difficult for a non-expert to select a good off-the-shelf parser.  ...  The spacy pretrain command lets you use transfer learning to initialize your models with information from raw text, using a language model objective similar to the one used in Google's BERT system.  ... 
doi:10.5281/zenodo.4007408 fatcat:5gtwktyy6rchde2hyts6tzmyku

RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining [article]

Alexander Yalunin, Alexander Nesterov, Dmitriy Umerenkov
2022 arXiv   pre-print
This paper presents several BERT-based models for Russian language biomedical text mining (RuBioBERT, RuBioRoBERTa).  ...  With this pre-training, our models demonstrate state-of-the-art results on RuMedBench - Russian medical language understanding benchmark that covers a diverse set of tasks, including text classification  ...  The vast majority of these solutions use the transformer neural network models pretrained on large text corpora.  ... 
arXiv:2204.03951v1 fatcat:kkquuwiocza7bek6jso4f6rlcy
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