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A Development of Inflammatory Bowel Disease Pattern Identification Algorithm Using Case Series Data

Inflammatory Bowel Diseases Clinical Trial

Official title:
Herbal Medicine for Inflammatory Bowel Diseases: a Development of Pattern Identification Algorithm by Retrospective Analysis of Case Series Data

Clinical Trial Summary

This study aimed to identify inflammatory bowel disease (IBD) patterns based on presenting symptoms and to suggest algorithms for determining pattern and herbal prescriptions for corresponding patterns. The investigators collected symptom data of 67 IBD patients who achieved and maintained clinical remissions after they had taken herbal medicine prescriptions. Prescriptions were categorised into 5 patterns, which were named after main features and symptoms of included patients. Associations between presenting symptoms and patterns were visualised using a term frequency inverse document frequency (TF-IDF) method. Determining IBD patterns from symptoms of patients was analysed and charted by decision tree modeling.

Clinical Trial Description

Herbal prescriptions are one of the most sought complementary and alternative medicine treatment strategies for inflammatory bowel disease patients. However, variability in pattern identification of Traditional Chinese Medicine (TCM)/Traditional East Asian Medicine (TEAM) has been criticised. Using data of patients who achieved and maintained clinical remission after TCM/TEAM herbal medicine prescription, the investigators aimed to develop treatment algorithms refined by identified pattern and key symptoms which practitioners can easily discriminate.

Based on herbal prescriptions which induced clinical remission, IBD patients were divided into 5 patterns, i.e., Large intestine type, Water-dampness type, Respiratory type, Upper gastrointestinal (GI) tract type, and Coldness type. By term frequency-inverse document frequency (TF-IDF) method, the association between 22 symptoms that were described as indications of the herbal medicine prescriptions and 5 patterns were analysed. Decision tree modeling was used for prediction of relevant patterns from symptoms. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04296500
Study type Observational
Source Kyunghee University
Contact
Status Completed
Phase
Start date November 1, 2007
Completion date October 28, 2015
View Details
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