Recent advances in extracting and processing rich semantics from medical texts

Denecke, Kerstin; Harmelen, Frank van

doi:10.1016/j.artmed.2018.07.004

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Artificial intelligence is the general term of the modern technology of computer science [ 1 ], including image recognition [ 2 ], network analysis [ 3 ], and natural language processing [ 4 ]. Artificial intelligence technologies have been utilized in various fields of medicine, for example, automatic disease diagnosis [ 5 ], pathogenic network analysis [ 6 ], and biological text analysis [ 7 ] [ 8 ]. Meanwhile, Traditional Chinese Medicine (TCM) plays an important role and provides a unique theoretical and practical way to treat diseases for thousands of years in Chinese history.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

Zhang

Liu

Zhu

et al. 2020

Computational and Mathematical Methods in Medicine

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The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

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Section: Introductionmentioning

confidence: 99%

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

Zhang

Liu

Zhu

et al. 2020

Computational and Mathematical Methods in Medicine

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“…Many research and development efforts have been made in the field of medicine and education . More and more people from different areas are trying to facilitate the techniques from their respective areas to tackle tough issues in medicine and education such as human crowds, TCM state identification, tongue image segmentation, and fluid simulation issues.…”

Section: Introductionmentioning

confidence: 99%

Special issue: 2016 International Conference on Information Technology in Medicine and Education (ITME2016)

Chen

2019

Concurrency and Computation

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Summary This special issue contains selected high‐quality papers from the 2016 International Conference on Information Technology in Medicine and Education (ITME2016), which was held on December 23‐25, 2016 in Fuzhou, China. The ITME conference series aims to provide an international forum for the presentation and discussion of research and development trends regarding information technology in medicine and education. The ITME2016 attracted many international attendants, allowing deep discussion and the exchange of ideas and results related to ongoing research among attendants. This special issue contains nine papers based on those that were presented at ITME2016. Among these are those by Liu et al, Lin et al, Zhang et al, Lin et al, Zhou et al, Cai et al, Li et al, Zheng et al, and Dai et al, 2018. Research problems in these papers have been analyzed systematically, and for specific approaches or models, evaluation has been performed to demonstrate their feasibility and advantages. The papers were selected on this basis and also peer reviewed thoroughly. The nine papers reflect a vision of the area in state‐of‐art fashion.

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“…drug reaction assessment, cancer risk 21 ) Disease modeling (e.g. connections of brain regions in children with autism 22 ) Classification, clustering, segmentation Monitoring and control Telemedicine 23 Text analysis and language processing Speech intentions from online conversation 24 Medical text semantics 25 Statistics from death certificates 26 Automatic classification of radiological reports 27 Classification of multilingual biomedical documents 28 Relation classification in medical records 23 Natural language processing tasks (e.g. summarization, text classification, relation extraction) Devices and gadgets Internet of Health Things (IoHT) 29 Smart home and early anomaly detection in elderly 30 Portable devices and mobile applications Decision support and expert systems Retinopathy by arteriovenous ratio 31 Coronary bypass surgery vs coronary stenting 32 Diagnostics Diagnostic labeling 33 Diagnosis by pattern recognition 34 Fatigue by eye tracking 35 > Correlations between diseases Early anomaly detection in behavior 30 Early indicators of Parkinson's disease progression 36 Treatment Therapy (e.g.…”

mentioning

confidence: 99%