136 research outputs found
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
We compare the use of LSTM-based and CNN-based character-level word
embeddings in BiLSTM-CRF models to approach chemical and disease named entity
recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus
show that the use of either type of character-level word embeddings in
conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art
performance. However, the models using CNN-based character-level word
embeddings have a computational performance advantage, increasing training time
over word-based models by 25% while the LSTM-based character-level word
embeddings more than double the required training time.Comment: In Proceedings of the 9th International Workshop on Health Text
Mining and Information Analysis (LOUHI 2018), to appea
Named Entity Recognition Using BERT BiLSTM CRF for Chinese Electronic Health Records
As the generation and accumulation of massive electronic health records (EHR), how to effectively extract the valuable medical information from EHR has been a popular research topic. During the medical information extraction, named entity recognition (NER) is an essential natural language processing (NLP) task. This paper presents our efforts using neural network approaches for this task. Based on the Chinese EHR offered by CCKS 2019 and the Second Affiliated Hospital of Soochow University (SAHSU), several neural models for NER, including BiLSTM, have been compared, along with two pre-trained language models, word2vec and BERT. We have found that the BERT-BiLSTM-CRF model can achieve approximately 75% F1 score, which outperformed all other models during the tests
Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models
Contextual Relation Extraction (CRE) is mainly used for constructing a
knowledge graph with a help of ontology. It performs various tasks such as
semantic search, query answering, and textual entailment. Relation extraction
identifies the entities from raw texts and the relations among them. An
efficient and accurate CRE system is essential for creating domain knowledge in
the biomedical industry. Existing Machine Learning and Natural Language
Processing (NLP) techniques are not suitable to predict complex relations from
sentences that consist of more than two relations and unspecified entities
efficiently. In this work, deep learning techniques have been used to identify
the appropriate semantic relation based on the context from multiple sentences.
Even though various machine learning models have been used for relation
extraction, they provide better results only for binary relations, i.e.,
relations occurred exactly between the two entities in a sentence. Machine
learning models are not suited for complex sentences that consist of the words
that have various meanings. To address these issues, hybrid deep learning
models have been used to extract the relations from complex sentence
effectively. This paper explores the analysis of various deep learning models
that are used for relation extraction.Comment: This Paper Presented in the International Conference on FOSS
Approaches towards Computational Intelligence and Language TTechnolog on
February 2023, Thiruvananthapura
GNTeam at 2018 n2c2:Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries
Monitoring the administration of drugs and adverse drug reactions are key
parts of pharmacovigilance. In this paper, we explore the extraction of drug
mentions and drug-related information (reason for taking a drug, route,
frequency, dosage, strength, form, duration, and adverse events) from hospital
discharge summaries through deep learning that relies on various
representations for clinical named entity recognition. This work was officially
part of the 2018 n2c2 shared task, and we use the data supplied as part of the
task. We developed two deep learning architecture based on recurrent neural
networks and pre-trained language models. We also explore the effect of
augmenting word representations with semantic features for clinical named
entity recognition. Our feature-augmented BiLSTM-CRF model performed with
F1-score of 92.67% and ranked 4th for entity extraction sub-task among
submitted systems to n2c2 challenge. The recurrent neural networks that use the
pre-trained domain-specific word embeddings and a CRF layer for label
optimization perform drug, adverse event and related entities extraction with
micro-averaged F1-score of over 91%. The augmentation of word vectors with
semantic features extracted using available clinical NLP toolkits can further
improve the performance. Word embeddings that are pre-trained on a large
unannotated corpus of relevant documents and further fine-tuned to the task
perform rather well. However, the augmentation of word embeddings with semantic
features can help improve the performance (primarily by boosting precision) of
drug-related named entity recognition from electronic health records
BioRED: A Comprehensive Biomedical Relation Extraction Dataset
Automated relation extraction (RE) from biomedical literature is critical for
many downstream text mining applications in both research and real-world
settings. However, most existing benchmarking datasets for bio-medical RE only
focus on relations of a single type (e.g., protein-protein interactions) at the
sentence level, greatly limiting the development of RE systems in biomedicine.
In this work, we first review commonly used named entity recognition (NER) and
RE datasets. Then we present BioRED, a first-of-its-kind biomedical RE corpus
with multiple entity types (e.g., gene/protein, disease, chemical) and relation
pairs (e.g., gene-disease; chemical-chemical), on a set of 600 PubMed articles.
Further, we label each relation as describing either a novel finding or
previously known background knowledge, enabling automated algorithms to
differentiate between novel and background information. We assess the utility
of BioRED by benchmarking several existing state-of-the-art methods, including
BERT-based models, on the NER and RE tasks. Our results show that while
existing approaches can reach high performance on the NER task (F-score of
89.3%), there is much room for improvement for the RE task, especially when
extracting novel relations (F-score of 47.7%). Our experiments also demonstrate
that such a comprehensive dataset can successfully facilitate the development
of more accurate, efficient, and robust RE systems for biomedicine
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning
BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.
OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.
METHODS: We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.
RESULTS: Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.
CONCLUSIONS: Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning
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