Uncovering Insights into Sleep Disorders: A Python Analysis of MIMIC-IV Health Data
Have you ever wondered how factors like age, gender, BMI, and medical history can affect sleep disorders? In this article, we delve into the DREAMT dataset to uncover the relationships between various sleep disorders and participant characteristics.
The participants included in the study provide valuable insights into sleep apnea, snoring, restless legs syndrome, and more.
Our analysis will culminate in a detailed data report with visualization and meaningful conclusions.
To conduct this analysis, we will leverage Python libraries like Pandas, Numpy, Matplotlib, and Seaborn within a Jupyter notebook.
The dataset used is from DREAMT (Dataset for Real-time sleep stage EstimAtion using Multisensor wearable Technology) part of MIMIC-IV datasets hosted by PhysioNet.