A Development of Inflammatory Bowel Disease Pattern Identification Algorithm Using Case Series Data
Condition: Inflammatory Bowel Diseases
MeSH terms: Intestinal Diseases; Inflammatory Bowel Diseases
Study type: Observational

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.
    

Detailed 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.
    

Intervention name(s): Decision tree modeling
Intervention type(s): Other
Intervention description(s): 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.
Observational model: Case-Only
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Patients who had been diagnosed with IBD by gastroenterologist and achieved clinical
        remission after treatment with herbal medicine prescriptions
      
Keywords: Herbal prescription; Herbal medicine; Pattern identification; Algorithm; Decision tree; TF-IDF