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

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.

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Key information

Conditions

Inflammatory Bowel Diseases Digestive System Diseases Gastroenteritis Gastrointestinal Diseases Intestinal Diseases

Age range

15 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Acupuncture & Meridian Science Research Centre

Seoul, 02447, South Korea

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Diagnosis of IBD by gastroenterologist
Patients have achieved and maintained clinical remission of IBD symptoms after they took herbal prescriptions
Patients have provided written informed consent

Exclusion criteria

Details regarding any of 25 symptoms were omitted

Treatment and study plan

Decision tree modeling

Other

A decision tree analysis was employed to explore the process of decision-making on types of pattern based on the existence or nonexistence of a symptom. At the end of tree presented is the proportion of patients who are categorised into each pattern.

In this study, the classification was performed by applying the classification and regression tree (CART) algorithm using Scikit-learn package of Python, which performs a division using the Gini coefficient or the decrement of dispersion. The Gini coefficient is one of the tools for measuring entropy or diversity in each node and it measures the decrement by comparing the information entropy before and after separation. To avoid overfitting, the maximum number of leaf nodes was limited to four and the pruning method which complied with the principle of minimum description length was applied.

Primary outcomes

Accuracy of pattern identification algorithm

Time frame: Oct 2015

Pattern identification algorithm was suggested using a decision tree method. Decision tree method was employed to explore the process of decision making on types of pattern based on clinical features of patients.

Sponsors and collaborators

Lead sponsor

Hyangsook Lee, KMD, PhD

Other

Registry information

Official study title

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

Important dates

Study start: 2007
Primary completion: 2015
Study completion: 2015
First posted: Mar 5, 2020
Registry last updated: Mar 6, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed