An Insight Extraction System on BioMedical Literature with Deep Neural Networks

He, Hua; Ganjam, Kris; Jain, Navendu; Lundin, Jessica; White, Ryen W.; Lin, Jimmy

doi:10.18653/v1/d17-1285

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Neural networks have been widely used in relation extraction, such as in CNNs [29], and in Recurrent Neural Networks (RNN) [39]. He et al constructed a system with a novel deep neural network (DNN) to automatically infer associations in the biomedical-related literature [40]. Zeng et al proposed a novel model dubbed Piecewise Convolutional Neural Networks (PCNNs) with multi-instance learning [41].…”

Section: Relation Extractionmentioning

confidence: 99%

MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction

Shen

Song

et al. 2022

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The popularity of information technology has given rise to a growing interest in smart education and has provided the possibility of combining online and offline education. Knowledge graphs, an effective technology for knowledge representation and management, have been successfully utilized to manage massive educational resources. However, the existing research on constructing educational knowledge graphs ignores multiple modalities and their relationships, such as teacher speeches and their relationship with knowledge. To tackle this problem, we propose an automatic approach to construct multi-modal educational knowledge graphs that integrate speech as a modal resource to facilitate the reuse of educational resources. Specifically, we first propose a fine-tuned Bidirectional Encoder Representation from Transformers (BERT) model based on education lexicon, called EduBERT, which can adaptively capture effective information in the education field. We also add a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) to effectively identify educational entities. Then, the locational information of the entity is incorporated into BERT to extract the educational relationship. In addition, to cover the shortage of traditional text-based knowledge graphs, we focus on collecting teacher speech to construct a multi-modal knowledge graph. We propose a speech-fusion method that links these data into the graph as a class of entities. The numeric results show that our proposed approach can manage and present various modes of educational resources and that it can provide better education services.

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Section: Relation Extractionmentioning

confidence: 99%

MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction

Shen

Song

et al. 2022

Information

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“…Many clinical events can be detected from free text EHR notes by applying Recurrent Neural Network (RNN) architectures such as disorders, medications, tests, adverse drug effects [117], and patient data de-identification from EHRs [118]. Bidirectional RNNs / LSTMs have been successfully applied to several biomedical NLP tasks such as building models for the prediction of the missing punctuation in medical reports [119], the identification of biomedical events [120], the modeling of relational and contextual similarities between the named entities in biomedical articles to understand important information to provide appropriate treatment suggestions [121], the extraction of clinical concepts from EHR reports [122], and the recognition of named entities in clinical texts [123]. Many recent researches develop models using the embedded graph information for adverse drug reaction detection in social media data [124] by applying bidirectional LSTM transducer.…”

Section: Application Of Machine Learning and Deep Learning Techniques In The Biomedical Nlp Domainmentioning

confidence: 99%

Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review

Houssein¹,

Mohamed

Ali³

2021

IEEE Access

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The widespread use of electronic health records (EHR) systems in health care provides a large amount of real-world data, leading to new areas for clinical research. Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great amount of valuable clinical information. However, in a free-form text such as electronic health records, many clinical data are still hidden in a clinical narrative format. Therefore, the performance of biomedical NLP techniques is required to unlock the full potential of EHR data to convert a clinical narrative text automatically into structured clinical data. In this way, biomedical NLP applications can be used to direct clinical decisions, identify medical problems, and effectively postpone or avoid the occurrence of a disease. This review discusses the current literature on the secondary use of electronic health record data for clinical research on chronic diseases and addresses the potential, challenges, and applications of biomedical NLP techniques. We review some of the biomedical NLP methods and systems used over EHRs and give an overview of machine learning and deep learning methodologies used to process EHRs and improve the understanding of the patient's clinical records and the prediction of chronic diseases risk, providing a great chance to extract previously unknown clinical information. Moreover, this review summarizes the utilizing of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data. Finally, this review presents the future trends and challenges in the biomedical NLP.

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Clinical Natural Language Processing with Deep Learning

Hasan

Farri

2019

Data Science for Healthcare

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An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Cited by 4 publications

References 30 publications

MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction

MEduKG: A Deep-Learning-Based Approach for Multi-Modal Educational Knowledge Graph Construction

Machine Learning Techniques for Biomedical Natural Language Processing: A Comprehensive Review

Clinical Natural Language Processing with Deep Learning

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