• Journal of Korean Medical classics
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• v.33 no.4
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• pp.67-81
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• 2020

Objectives : This research aims to suggest a automatic data extract method for herbal formula combinations from medical classics' texts. Methods : This research was carried out by using Access of Microsoft Office 365 in Windows 10 of Microsoft. The subject text for extraction was 『Euijongsonik』. Using data sets of herb and dosage terminology, herbal medicinals and their dosages were extracted. Afterwards, using the position value of the character string, the formula combinations were automatically extracted. Results :The PC environment of this research was Intel Core i7-1065G7 CPU 1.30GHz, with 8GB of RAM and a Windows 10 64bit operation system. Out of 6,115 verses, 19,277 herb-dosage combinations were extracted. Conclusions : In this research, it was demonstrated that in the case of classical texts that are available as data, knowledge on herbal medicine could be extracted without human or material resources. This suggests an applicability of classical text knowledge to clinical practice.

• Journal of Korean Medical classics
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• v.33 no.4
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• pp.21-32
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• 2020

Objectives : Pulse descriptions in Korean Medical texts are comprised of combinations of pulse terminology, where various combinations of pulse terminology are used to describe disease symptoms. For Korean Medical doctors and professionals, however, it is impossible to identify the entirety of pulse description combinations, and their understanding is mostly limited to those learned from classical texts studied individually. Methods :This research was carried out by using Access of Microsoft Office 365 in Windows 10 of Microsoft. Pulse descriptions were extracted from the text, 『Euijongsonik』. In the final stages, the automatically extracted list of pulse descriptions was refined through [excluded terminology of pulse description]. Results : The PC environment of this research was Intel Core i7-1065G7 CPU 1.30GHz, with 8GB of RAM and a Windows 10 64bit operation system. Out of 6,115 verses 6,497 descriptions were primarily extracted, and after a refinement process, the final list contained 5,507 pulse descriptions. Conclusions : Based on the assumption that classical texts are available in data form to be processed by programs, current research methodology demonstrated that it was more efficient in regards to time and man power to create a pulse description database compared to when the researcher manually created one.