At a glance

ClinicalIndex Comparison Record

N/ACompleted· 67 enrolled

Drug / intervention

Decision tree modelingother

Likely dose

Not stated in record

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Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.

Search/NCT04296500

NCT04296500N/ACompleted

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

Hyangsook Lee, KMD, PhD·observational·Posted Mar 5, 2020·Updated Mar 6, 2020

In Brief

An observational study evaluating Decision tree modeling for Inflammatory Bowel Diseases. Completed, enrolled 67 participants across 1 site.

Detailed 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.

Study Details

Study Typeobservational

Allocation--

Masking--

Primary Purpose--

ConditionsInflammatory Bowel Diseases

CountriesSouth Korea

Collaborators--

Timeline

N/ACompletedFinished

20082009201020112012201320142015201620172018201920202021202220232024202520262027

Enrollment StartNov 2007

Primary CompletionFeb 2015

Study CompletionOct 2015

First PostedMar 2020

TodayJul 2026

First PostedMar 5, 2020

Enrollment StartNov 1, 2007

Primary CompletionFeb 28, 2015

Study CompletionOct 28, 2015

TodayJul 2, 2026

Enrollment to primary: 7.3 yearsPosted 6.3 years ago

Interventions

Decision tree modelingother

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.