A Python evaluation of a MIMIC-IV well being knowledge (DREAMT) to uncover insights into components affecting sleep problems.
20 hours in the past
On this article, I will likely be analysing individuals’ info from the DREAMT dataset with a purpose to uncover relationships between sleep problems 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 individuals listed below are those that took half within the DREAMT examine.
The result will likely be a complete knowledge analytics report with visualizations, insights, and conclusion.
I will likely 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.
