A systematic literature review and classification of knowledge discovery in traditional medicine

Arji, Goli; Safdari, Reza; Rezaeizadeh, Hossein; Abbassian, Alireza; Mokhtaran, Mehrshad; Ayati, Mohammad Hossein

doi:10.1016/j.cmpb.2018.10.017

Cited by 55 publications

(26 citation statements)

References 120 publications

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“…Since the existing disease-pertinent relationship extraction is mostly based on electronic medical records [4]- [6], discharge abstracts [7]- [10] and medical literature abstracts [5], [11], [12], the greatest advantage of our research is the unstructured, large-scale physician-patient Q&A data in OHCs, which realized the best results by our model. Additionally, the domain-related word embeddings that we trained and the attention mechanisms based on character-level and sentence-level play an important role in relation extraction.…”

Section: Discussionmentioning

confidence: 99%

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Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Zhang

2020

IEEE Access

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Relationship extraction among diseases, symptoms and tests has always been a concerning research issue in the biomedical field. Disease-pertinent relationship extraction for user-generated content in the online health community represents a research trend. By training the word embedding vectors for the medical-health field, conducting entity recognition and relationship annotation, and using deep learning technology, we construct a relation extraction model for extracting the relationships among diseases, symptoms and tests. Our relationship extraction model of the bidirectional gate recurrent unit (BiGRU) network based on character-level and sentence-level attention mechanisms achieved the best results on question-answer data in the online health community. Our research results can not only help physician diagnoses but also help patients perform health management, which has important industrial application value. INDEX TERMS Knowledge extraction, relationship extraction, attention mechanism, deep learning, bidirectional gated recurrent unit (BiGRU), online health community.

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Section: Discussionmentioning

confidence: 99%

“…There are many studies on relationship extraction in the biomedical health domain, but most of them are based on electronic medical records [4]- [6], discharge abstracts [7]- [10] and medical literature abstracts [5], [11], [12]. The words and sentences in these corpora are relatively rigorous and standardized.…”

Section: Introductionmentioning

confidence: 99%

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Zhang

2020

IEEE Access

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“…Performance evaluation procedures are among the furthermost significant indicators for determining the quality of the artificial intelligence techniques [40]. Overall, performance evaluation procedures are classified into two main types; the first being the single scaler techniques and second being the graphical techniques.…”

Section: 4mentioning

confidence: 99%

“…Performance evaluation procedures are usually employed as a valued method in determining the quality of the numerous fuzzy techniques [40]. As shown in Fig.…”

Section: 3mentioning

confidence: 99%

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Arji

Ahmadi

Nilashi

et al. 2019

Biocybernetics and Biomedical Engineering

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“…Fayyad et al described the methodical approach Knowledge Discovery in Databases (KDD) in 1996 and stated that ''it is only natural to turn to computational techniques to help us unearth meaningful patterns and structures from the massive volumes of data,'' as well as that ''practical computational constraints place severe limits on the subspace that can be explored by a data-mining algorithm'' [23]. The applied techniques from KDD, such as ''data mining,'' ''artificial neural networks'' or ''support vector machines,'' as well as the process steps ''preparation,'' ''modeling'' and ''postprocessing,'' can be found in the technical implementations of a systematic literature review (SLR), with the result that the approaches described by Arji et al can be applied [24]. At first, it may appear that limitations from the past can be neglected due to technological advancements and cloud computing, but in comparison to the past, the amount of data to be analyzed has become more extensive [1], [2].…”

Section: Background and Related Workmentioning

confidence: 99%

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

et al. 2019

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To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a ''double funnel of artificial intelligence.'' Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a ''classical'' literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from ''regular'' review papers; however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed. INDEX TERMS Computational and artificial intelligence, document handling, fuzzy control, knowledge acquisition, pattern analysis, scientific publishing, text mining, text processing.

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A systematic literature review and classification of knowledge discovery in traditional medicine

Cited by 55 publications

References 120 publications

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

The Application of Artificial Intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

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