Crohn's Disease Treatment Prediction Model

DB for Machine learning using clinical data at baselines. Used to predicts the medium-term efficacy of biologic therapies for in patients with Crohn's Disease. 1. Data Collection Sources - Electronic Health Records (EHR) - Clinical trials and studies - Genetic data - Patient-reported outcomes - Medical imaging Types of Data - Demographic information - Clinical data (symptoms, disease severity, treatment history) - Genetic data (SNPs, mutations) - Lab results (CRP levels, fecal calprotectin) - Imaging data (MRI, endoscopy) - Lifestyle data (diet, smoking status) 2. Data Preprocessing Steps - Data Cleaning: Handle missing values, remove duplicates, correct errors. - Data Normalization/Standardization: Normalize lab results, standardize imaging data. - Feature Engineering: Create new features from existing data, e.g., calculate disease activity scores. - Encoding Categorical Data: Convert categorical variables to numerical ones using one-hot encoding or label encoding. - Data Splitting: Split data into training, validation, and test sets.