BERT efficacy on scientific and medical datasets: a systematic literature review

Bidirectional Encoder Representations from Transformers (BERT) [Devlin et al., 2018] has been shown to be effective at modeling a multitude of datasets across a wide variety of Natural Language Processing (NLP) tasks; however, little research has been done regarding BERT’s effectiveness at modeling domain-specific datasets. Specifically, scientific and medical datasets present a particularly difficult challenge in NLP, as these types of corpora are often rife with technical jargon that is largely absent from the canonical corpora that BERT and other transfer learning models were originally trained on. This thesis is a Systematic Literature Review (SLR) of twenty-seven studies that were selected to address the various methods of implementation when applying BERT to scientific and medical datasets. These studies show that despite the datasets’ esoteric subject matter, BERT can be effective at a wide range of tasks when applied to domain-specific datasets. Furthermore, these studies show that the addition of domain-specific pretraining, either through additional pretraining or the utilization of domain-specific BERT derivatives such as BioBERT [Lee et al., 2019], can further augment BERT’s performance on scientific and medical texts

Named Entity Recognition in Electronic Health Records: A Methodological Review

Objectives A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. Methods We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. Results Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. Conclusions EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice

A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing

Language models (LMs) like BERT and GPT have revolutionized natural language processing (NLP). However, privacy-sensitive domains, particularly the medical field, face challenges to train LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring the preservation of data privacy. In this study, we systematically evaluate FL in medicine across $2$ biomedical NLP tasks using $6$ LMs encompassing $8$ corpora. Our results showed that: 1) FL models consistently outperform LMs trained on individual client's data and sometimes match the model trained with polled data; 2) With the fixed number of total data, LMs trained using FL with more clients exhibit inferior performance, but pre-trained transformer-based models exhibited greater resilience. 3) LMs trained using FL perform nearly on par with the model trained with pooled data when clients' data are IID distributed while exhibiting visible gaps with non-IID data. Our code is available at: https://github.com/PL97/FedNLPComment: Accepted by KDD 2023 Workshop FL4Data-Minin

Integration of natural and deep artificial cognitive models in medical images: BERT-based NER and relation extraction for electronic medical records

IntroductionMedical images and signals are important data sources in the medical field, and they contain key information such as patients' physiology, pathology, and genetics. However, due to the complexity and diversity of medical images and signals, resulting in difficulties in medical knowledge acquisition and decision support.MethodsIn order to solve this problem, this paper proposes an end-to-end framework based on BERT for NER and RE tasks in electronic medical records. Our framework first integrates NER and RE tasks into a unified model, adopting an end-to-end processing manner, which removes the limitation and error propagation of multiple independent steps in traditional methods. Second, by pre-training and fine-tuning the BERT model on large-scale electronic medical record data, we enable the model to obtain rich semantic representation capabilities that adapt to the needs of medical fields and tasks. Finally, through multi-task learning, we enable the model to make full use of the correlation and complementarity between NER and RE tasks, and improve the generalization ability and effect of the model on different data sets.Results and discussionWe conduct experimental evaluation on four electronic medical record datasets, and the model significantly out performs other methods on different datasets in the NER task. In the RE task, the EMLB model also achieved advantages on different data sets, especially in the multi-task learning mode, its performance has been significantly improved, and the ETE and MTL modules performed well in terms of comprehensive precision and recall. Our research provides an innovative solution for medical image and signal data

Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

Contributions to information extraction for spanish written biomedical text

285 p.Healthcare practice and clinical research produce vast amounts of digitised, unstructured data in multiple languages that are currently underexploited, despite their potential applications in improving healthcare experiences, supporting trainee education, or enabling biomedical research, for example. To automatically transform those contents into relevant, structured information, advanced Natural Language Processing (NLP) mechanisms are required. In NLP, this task is known as Information Extraction. Our work takes place within this growing field of clinical NLP for the Spanish language, as we tackle three distinct problems. First, we compare several supervised machine learning approaches to the problem of sensitive data detection and classification. Specifically, we study the different approaches and their transferability in two corpora, one synthetic and the other authentic. Second, we present and evaluate UMLSmapper, a knowledge-intensive system for biomedical term identification based on the UMLS Metathesaurus. This system recognises and codifies terms without relying on annotated data nor external Named Entity Recognition tools. Although technically naive, it performs on par with more evolved systems, and does not exhibit a considerable deviation from other approaches that rely on oracle terms. Finally, we present and exploit a new corpus of real health records manually annotated with negation and uncertainty information: NUBes. This corpus is the basis for two sets of experiments, one on cue andscope detection, and the other on assertion classification. Throughout the thesis, we apply and compare techniques of varying levels of sophistication and novelty, which reflects the rapid advancement of the field

Transfer learning: bridging the gap between deep learning and domain-specific text mining

Inspired by the success of deep learning techniques in Natural Language Processing (NLP), this dissertation tackles the domain-specific text mining problems for which the generic deep learning approaches would fail. More specifically, the domain-specific problems are: (1) success prediction in crowdfunding, (2) variants identification in biomedical literature, and (3) text data augmentation for domains with low-resources. In the first part, transfer learning in a multimodal perspective is utilized to facilitate solving the project success prediction on the crowdfunding application. Even though the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text modality. It is promising to utilize the visual images in project profiles to find out how images could contribute to the success prediction. An advanced neural network scheme is designed and evaluated combining information learned from different modalities for project success prediction. In the second part, transfer learning is combined with deep learning techniques to solve genomic variants Named Entity Recognition (NER) problems in biomedical literature. Most of the advanced generic NER algorithms can fail due to the restricted training corpus. However, those generic deep learning algorithms are capable of learning from a canonical corpus, without any effort on feature engineering. This work aims to build an end-to-end deep learning approach to transfer the domain-specific knowledge to those advanced generic NER algorithms, addressing the challenges in low-resource training and requiring neither hand-crafted features nor post-processing rules. For the last part, transfer learning with knowledge distillation and active learning are utilized to solve text augmentation for domains with low-resources. Most of the recent text augmentation methods heavily rely on large external resources. This work is dedicates to solving the text augmentation problem adaptively and consistently with minimal resources for token-level tasks like NER. The solution can also assure the reliability of machine labels for noisy data and can enhance training consistency with noisy labels. All the works are evaluated on different domain-specific benchmarks, respectively. Experimental results demonstrate the effectiveness of those proposed methods. The advantages also indicate promising potential for transfer learning in domain-specific applications

Biomedical entities recognition in Spanish combining word embeddings

El reconocimiento de entidades con nombre (NER) es una tarea importante en el campo del Procesamiento del Lenguaje Natural que se utiliza para extraer conocimiento significativo de los documentos textuales. El objetivo de NER es identificar trozos de texto que se refieran a entidades específicas. En esta tesis pretendemos abordar la tarea de NER en el dominio biomédico y en español. En este dominio las entidades pueden referirse a nombres de fármacos, síntomas y enfermedades y ofrecen un conocimiento valioso a los expertos sanitarios. Para ello, proponemos un modelo basado en redes neuronales y empleamos una combinación de word embeddings. Además, nosotros generamos unos nuevos embeddings específicos del dominio y del idioma para comprobar su eficacia. Finalmente, demostramos que la combinación de diferentes word embeddings como entrada a la red neuronal mejora los resultados del estado de la cuestión en los escenarios aplicados.Named Entity Recognition (NER) is an important task in the field of Natural Language Processing that is used to extract meaningful knowledge from textual documents. The goal of NER is to identify text fragments that refer to specific entities. In this thesis we aim to address the task of NER in the Spanish biomedical domain. In this domain entities can refer to drug, symptom and disease names and offer valuable knowledge to health experts. For this purpose, we propose a model based on neural networks and employ a combination of word embeddings. In addition, we generate new domain- and language-specific embeddings to test their effectiveness. Finally, we show that the combination of different word embeddings as input to the neural network improves the state-of-the-art results in the applied scenarios.Tesis Univ. Jaén. Departamento de Informática. Leída el 22 abril de 2021