A Python evaluation of a MIMIC-IV well being information (DREAMT) to uncover insights into components affecting sleep issues.
20 hours in the past
On this article, I might be analysing contributors’ data from the DREAMT dataset in an effort to uncover relationships between sleep issues like sleep apnea, loud night breathing, issue 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 contributors listed here are those that took half within the DREAMT research.
The result might be a complete information 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 information getting used for this evaluation comes from DREAMT: Dataset for Actual-time sleep stage EstimAtion utilizing Multisensor wearable Expertise 1.0.1. DREAMT is a part of the MIMIC-IV datasets hosted by PhysioNet.
