A Python evaluation of a MIMIC-IV well being information (DREAMT) to uncover insights into elements affecting sleep issues.
On this article, I shall be analysing members’ info from the DREAMT dataset in an effort to uncover relationships between sleep issues like sleep apnea, loud night breathing, problem respiration, complications, Stressed Legs Syndrome (RLS), snorting and participant traits like age, gender, Physique Mass Index (BMI), Arousal Index, Imply Oxygen Saturation (Mean_SaO2), medical historical past, Obstructive apnea-hypopnea index (OAHI) and Apnea-Hypopnea Index (AHI).
The members listed here are those that took half within the DREAMT research.
The result shall be a complete information analytics report with visualizations, insights, and conclusion.
I shall be using a Jupyter pocket book with Python libraries like Pandas, Numpy, Matplotlib and Seaborn.
The information getting used for this evaluation comes from DREAMT: Dataset for Actual-time sleep stage EstimAtion utilizing Multisensor wearable Know-how 1.0.1. DREAMT is a part of the MIMIC-IV datasets hosted by PhysioNet.