- P-ISSN 1010-0695
- E-ISSN 2288-3339
- Log In/Sign Up
- P-ISSN1010-0695
- E-ISSN2288-3339
Article Detail
Home > Article Detail
Application of text-mining technique and machine-learning model with clinical text data obtained from case reports for Sasang constitution diagnosis:a feasibility study
Journal of Korean Medicine / Journal of Korean Medicine, (P)1010-0695; (E)2288-3339
2024, v.45 no.3, pp.193-210
Jinseok,
K.
, So-hyun,
P.
, Roa,
J.
, Eunsu,
L.
, Yunseo,
K.
, Hyundong,
S.
, &
Jun-Sang,
Y.
(2024). Application of text-mining technique and machine-learning model with clinical text data obtained from case reports for Sasang constitution diagnosis:a feasibility study. Journal of Korean Medicine, 45(3), 193-210.
- Downloaded
- Viewed
Abstract
Objectives: We analyzed Sasang constitution case reports using text mining to derive network analysis results and designed a classification algorithm using machine learning to select a model suitable for classifying Sasang constitution based on text data. Methods: Case reports on Sasang constitution published from January 1, 2000, to December 31, 2022, were searched. As a result, 343 papers were selected, yielding 454 cases. Extracted texts were pretreated and tokenized with the Python-based KoNLPy package. Each morpheme was vectorized using TF-IDF values. Word cloud visualization and centrality analysis identified keywords mainly used for classifying Sasang constitution in clinical practice. To select the most suitable classification model for diagnosing Sasang constitution, the performance of five models—XGBoost, LightGBM, SVC, Logistic Regression, and Random Forest Classifier—was evaluated using accuracy and F1-Score. Results: Through word cloud visualization and centrality analysis, specific keywords for each constitution were identified. Logistic regression showed the highest accuracy (0.839416), while random forest classifier showed the lowest (0.773723). Based on F1-Score, XGBoost scored the highest (0.739811), and random forest classifier scored the lowest (0.643421). Conclusions: This is the first study to analyze constitution classification by applying text mining and machine learning to case reports, providing a concrete research model for follow-up research. The keywords selected through text mining were confirmed to effectively reflect the characteristics of each Sasang constitution type. Based on text data from case reports, the most suitable machine learning models for diagnosing Sasang constitution are logistic regression and XGBoost.
- keywords
- Data Mining, Machine Learning, Case reports, Sasang Constitutional Medicine
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations
Other articles from this issue
- Factors influencing the use of traditional Korean herbal medicine - based on the 2020 Korean Medical Panel data
- General prescription pattern of insured herbal preparation in South Korea:A nationwide cohort study
- The Characteristics of the Subject and Relationship between the Intention to Use Hospice and Allowance of Pre-accession based on the Advance Directives Registered by Korean Medicine Clinic
- Acute toxicity and genotoxicity assessment of Ligigeopoong-san
- Adverse event reports of tonifying herbal medicine products
- A study on the improvement for the classification of Korean Medicine industry
- A Scoping Review of Clinical Study in Pelvic Congestion Syndrome Focused on Blood Stasis
- A Comparative Analysis of Traditional Korean and Chinese Medicine Treatments for Lung Cancer : from a Cancer Treatment Book and Case Reports
- The Clinical Suggestion of Seunggi-Tang by Reviewing Therapeutic Methods
- Pilot application and Comparative Study between inter-raters in Clinical Performance Examination (CPX) of Ankle Sprain
- Analyzing Trend of Clinic-Level Medical Institutions and Pharmacy Name Using Text Mining Technique
- Analysis of the Status of Convalescent Hospitals Certified from January to March 2024: Focus on Korean Medicine Services
- Protective effects of Puriton® on acute pancreatitis
- The Effect of Rhus verniciflua Stokes Extract on Apoptosis and Autophagy in human leukemic cell line (MOLT-4)
- A survey on the perception of the role of public health doctors of Korean medicine due to regional health care gaps
- A Survey Study on the Effect of Medical Volunteer Activities on Competency Enhancement and the Development of Medical Volunteer Guidelines for Korean Medicine Students
- Application of text-mining technique and machine-learning model with clinical text data obtained from case reports for Sasang constitution diagnosis:a feasibility study
- Korean Medicine Treatment with Scalp Acupuncture for Diplopia and Lateropulsion in a Patient with Lateral Medullary Infarction: A Case Report
- 3 Successful Case Reports of Tinnitus with Thread-embedding therapy
- Principle of major acupoints composition: focus on Four Command and Star Twelve points
- more
Recommanded Articles
This website recommended the use the Chrome browser for journal website.