J Korean Med Assoc.  2012 Aug;55(8):741-747. 10.5124/jkma.2012.55.8.741.

Medical informatics methods for the clinical evidence extraction

• Song MH
• Park DK
• Lee YH

• KMID: 1435180
• DOI: http://doi.org/10.5124/jkma.2012.55.8.741

Abstract

Clinical professionals gain new information to assist in patient care when they read the medical literature. Similarly, in clinical preventive medicine, medical science documents that have previously published can be searched and evaluated in order to confirm the scientific support for the clinical preventive medical service offered in order to prevent chronic disease. This paper introduces the medical informatics techniques for knowledge extraction that can become the basis for clinical practice. Particularly, it discusses the clinical document retrieval and knowledge discovery tools that can search for extracting the knowledge which the medical expert desires with data mining techniques. For example, Clinical medical personnel and medical researchers can locate the information from the latest literature rapidly or find and evaluate the scientific basis for the treatment and prevention of infection. This study can be used when they analyze the correlation between accumulated and different type of data and contributes to the detection of new knowledge. Recently, the concern about the visualization of massive data and information is high as the importance of big data has received greater attention. Contributions to this technique and decision support tools will increase gradually due to the way support for decision-making through scientific evidence for the pattern changing disease is evaluated or as one of the clinical practice guidelines is accepted.

Keyword

Evidence-based medicine; Medical informatics computing; Decision support techniques; Information storage and retrieval; Artificial intelligence

MeSH Terms

Artificial Intelligence
Chronic Disease
Data Mining
Decision Support Techniques
Evidence-Based Medicine
Information Storage and Retrieval
Medical Informatics
Medical Informatics Computing
Patient Care
Preventive Medicine

Figure

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  Figure 1 PathwayAssist graphical interface. Pathway assist enables researchers to create their own pathways and produce publication quality pathway diagrams. Pathway-Assist uses a proprietary graph visualization engine to allow for the visual features (From Nikitin A, et al. Bioinformatics 2003;19:2155-2157, with permission from Oxford University Press) [22].

• 

  Figure 2 AliBaba Graphical Interface. (Parts of the) graph resulting from five PubMed ab-stracts for the query 'FADD'. Information on the selected protein 'caspase-8' is given in the right panel, for instance, association partners and evidence texts (From Plake C, et al. Bioinformatics 2006;22:2444-2445, with permission from Oxford University Press) [25].

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