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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Inflammatory Bowel Diseases

Intervention / Treatment
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* Other: Decision tree modeling


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 15 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Decision tree algorithm training/testing<br>The investigators divided data of 67 patients into 5 groups to do 5 fold cross validation. Four groups were used to train decision tree algorithm and one group was used to test it. | Other: Decision tree modeling<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of pattern identification algorithm | 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. | Oct 2015 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Herbal prescription, Herbal medicine, Pattern identification, Algorithm, Decision tree, TF-IDF