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

Hyangsook Lee, KMD, PhD

Status: Completed

Conditions: Inflammatory Bowel Diseases

Treatments: Other: Decision tree modeling

Study type: Observational

Funder types: Other

Identifiers

NCT04296500

KyungheeU

Details and patient eligibility

About

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.

Full 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.

Enrollment: 67 patients

Sex: All

Ages: 15 to 65 years old

Volunteers: No Healthy Volunteers

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

Trial design

67 participants in 1 patient group

Decision tree algorithm training/testing

Description:

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.

Treatment:

Other: Decision tree modeling