A Python evaluation of a MIMIC-IV well being knowledge (DREAMT) to uncover insights into components affecting sleep problems.
On this article, I might be analysing contributors’ info from the DREAMT dataset so as to uncover relationships between sleep problems like sleep apnea, loud night breathing, problem respiratory, 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 contributors listed below are those that took half within the DREAMT research.
The result might be a complete knowledge analytics report with visualizations, insights, and conclusion.
I might be using a Jupyter pocket book with Python libraries like Pandas, Numpy, Matplotlib and Seaborn.
The info 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.
