Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records

Zhao, Qing; Xu, Da-Zhong; Li, Jianqiang; Zhao, Lei; Rajput, Faheem Akhtar

doi:10.1016/j.eswa.2022.117606

Cited by 19 publications

(5 citation statements)

References 23 publications

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“…A biRNN effectively extracts input and future (positive and negative) information simultaneously for a specific timeframe, which is not the case with regular RNNs [137]. NER performance is enhanced with limited data by adding knowledge and data augmentation via a multi-task bidirectional RNN model combined with deep transfer learning [138,139]. The RNN encoder-decoder encodes a sequence of symbols into a static length vector representation of multi-RNN neural networks and symbols [140].…”

Section: Tag Encoder-decoder Architecturementioning

confidence: 99%

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

2023

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Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, places, and organizations, as predefined semantic types in electronic health records (EHR). bNER is essential for discovering novel knowledge using computational methods and Information Technology. Early bNER systems were configured manually to include domain-specific features and rules. However, these systems were limited in handling the complexity of the biomedical text. Recent advances in deep learning (DL) have led to the development of more powerful bNER systems. DL-based bNER systems can learn the patterns of biomedical text automatically, making them more robust and efficient than traditional rule-based systems. This paper reviews the healthcare domain of bNER, using DL techniques and artificial intelligence in clinical records, for mining treatment prediction. bNER-based tools are categorized systematically and represent the distribution of input, context, and tag (encoder/decoder). Furthermore, to create a labeled dataset for our machine learning sentiment analyzer to analyze the sentiment of a set of tweets, we used a manual coding approach and the multi-task learning method to bias the training signals with domain knowledge inductively. To conclude, we discuss the challenges facing bNER systems and future directions in the healthcare field.

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Section: Tag Encoder-decoder Architecturementioning

confidence: 99%

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

2023

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“…Knowledge graphs cover a more comprehensive range of knowledge than traditional expert systems. To support the construction of knowledge graphs, existing studies have identified entities [ 42 – 44 ] and relationships [ 45 – 47 ] between drugs and diseases from various data sources. In downstream tasks, knowledge graphs can be combined with algorithms such as machine learning to achieve prescription recommendations [ 25 – 27 ].…”

Section: Related Workmentioning

confidence: 99%

Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval

Ding,

Qiao,

Zhang

2023

Plant Methods

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Background In the era of Agri 4.0 and the popularity of Plantwise systems, the availability of Plant Electronic Medical Records has provided opportunities to extract valuable disease information and treatment knowledge. However, developing an effective prescription recommendation method based on these records presents unique challenges, such as inadequate labeling data, lack of structural and linguistic specifications, incorporation of new prescriptions, and consideration of multiple factors in practical situations. Results This study proposes a plant disease prescription recommendation method called PRSER, which is based on sentence embedding retrieval. The semantic matching model is created using a pre-trained language model and a sentence embedding method with contrast learning ideas, and the constructed prescription reference database is retrieved for optimal prescription recommendations. A multi-vegetable disease dataset and a multi-fruit disease dataset are constructed to compare three pre-trained language models, four pooling types, and two loss functions. The PRSER model achieves the best semantic matching performance by combining MacBERT, CoSENT, and CLS pooling, resulting in a Pearson coefficient of 86.34% and a Spearman coefficient of 77.67%. The prescription recommendation capability of the model is also verified. PRSER performs well in closed-set testing with Top-1/Top-3/Top-5 accuracy of 88.20%/96.07%/97.70%; and slightly worse in open-set testing with Top-1/Top-3/Top-5 accuracy of 82.04%/91.50%/94.90%. Finally, a plant disease prescription recommendation system for mobile terminals is constructed and its generalization ability with incomplete inputs is verified. When only symptom information is available without environment and plant information, our model shows slightly lower accuracy with Top-1/Top-3/Top-5 accuracy of 75.24%/88.35%/91.99% in closed-set testing and Top-1/Top-3/Top-5 accuracy of 75.08%/87.54%/89.84% in open-set testing. Conclusions The experiments validate the effectiveness and generalization ability of the proposed approach for recommending plant disease prescriptions. This research has significant potential to facilitate the implementation of artificial intelligence in plant disease treatment, addressing the needs of farmers and advancing scientific plant disease management.

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“…The α 1 is the learning rate of the learning task and the parameters of the model, and the optimized performance after meta-training f θ ′ , see equation ( 9).…”

Section: Integration Of Graphs and Meta-learningmentioning

confidence: 99%

“…According to the research of related scholars, graph neural networks have three general frameworks: the first one, message passing neural network (MPNN) [4], unified graph neural network, and graph convolutional network (GCN) method [5]; The second nonlocal neural network (NLNN) [6] combines various Attention mechanisms; the last graph network (GN) unifies the first two MPNN and NLNN, such as GGNN model, relation network (Relation Network citeRelationN, Interaction Networks, etc. However, although these network models perform well in large-sample learning, they still perform poorly in smallsample professional vocabulary representation learning such as electronic medical record representation, Chinese bioinformatics relationship extraction, electronic health record information extraction, medical patient diagnosis classification, etc [8] [9] [10] [11]. The text corpus of medical professional vocabulary is generally small and beyond the expertise of most people, and requires a certain level of medical professional background to understand.…”

Section: Introductionmentioning

confidence: 99%

MetaGNN-Based Medical Records Unstructured Specialized Vocabulary Few-Shot Representation Learning

Ling

Luo

Yu³

2022

IEEE Access

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With the continuous breakthroughs in artificial intelligence technology, it has become easier to extract general-purpose knowledge using machine learning, but it is a challenging task to extract and learn small samples of knowledge in medical expertise. On the one hand, it is difficult to represent medical expertise entities, and on the other hand, the training samples of such expertise are small, and deep learning methods often require a large number of samples to complete the learning task. To this end, we proposes a graph network learning method for specialized vocabulary representation. Specifically, a contextual knowledge representation model based on graph meta-learning is proposed, which combines text, phrase, vocabulary, and other information to solve the problem of sparse data of medical electronic medical record entities that cannot be extracted and learned. In this method, a text-independent lexical representation learning method, a context-aware graph neural network, and a combined LSTM language model are used to model information from different perspectives as a way to learn semantic representations of professional discourse entities. The experimental results show that the accuracy of the method outperforms other similar methods and proves its effectiveness.

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Knowledge guided distance supervision for biomedical relation extraction in Chinese electronic medical records

Cited by 19 publications

References 23 publications

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain

Plant disease prescription recommendation based on electronic medical records and sentence embedding retrieval

MetaGNN-Based Medical Records Unstructured Specialized Vocabulary Few-Shot Representation Learning

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