Data_Validation of Patient data in Healthcare and Life Sciences Using Pydantic

This project provides a robust, extensible data validation model using Pydantic v2 to validate and enrich clinical trial subject demographic data.

The model ensures that every data record is:

1. Accurate (valid types, required fields)

2. Clean (case-insensitive enums, defaults)

3. Validated (cross-checks like BMI)

✅ Features

1. subject_id must be an 8-digit or longer positive integer

2. sex supports "Male" / "Female" input in any case (e.g. MALE, male)

3. ethnicity defaults to "American" if not provided

4. bmi must match the expected value computed from weight and height

Auto-computed fields:

1. expected_bmi

2. bmi_category (e.g., Normal, Overweight)

📦 Tech Stack

-- Python 3.9+

-- Pydantic v2

-- Clean and simple to extend into FastAPI or Pandas pipelines

📦 Sample Input

from model import Demographics

data = { "subject_id": 10011004, "age": 33, "sex": "female", "race": "Japanese", "weight_kg": 51, "height_cm": 155.5, "bmi": 21.4 }

demo = Demographics(**data) print(demo.model_dump())