📄️ 3.1 Handling Missing Values in Clinical Data
Before we get into explorating our data, we need to ensure we have a good handle on 'missingness'.
📄️ 3.2 Outlier Detection and Handling in Health Data
After handling missingness, we can now move on to outliers, and then finally the EDA.
📄️ 3.3 Understanding Health Data Distribution
Now that we have reviewed our data for missingness and outliers, we are ready to begin looking at the distributions of our data elements.
📄️ 3.4 Automated Exploratory Data Analysis (EDA) with Python
Automated Exploratory Data Analysis (EDA) tools can significantly speed up the process of understanding your health data's distribution, identifying patterns, and gaining insights. These tools often provide a comprehensive overview of your data's characteristics without requiring manual scripting. Here are a few popular Python packages that can assist in automated EDA:
📄️ 3.5 Correlations and Covariances in Medical Metrics
In healthcare, understanding relationships between medical metrics is invaluable for clinical decision-making and research.
📄️ 3.6 Population Level Health Analysis
Population-level health analysis involves examining healthcare data on a broader scale to derive insights about health trends, disease prevalence, and population characteristics. This type of analysis is crucial for public health planning, resource allocation, and policy development. In this section, we'll delve into techniques and concepts used for population-level health analysis.
📄️ 3.7 Visualization Techniques in Health EDA
Visualizing healthcare data is an essential step in exploratory data analysis (EDA). Visualization techniques provide insights that raw numbers often fail to convey. By creating meaningful visual representations, patterns, trends, and anomalies within the data can be easily spotted, leading to better decision-making and actionable insights.
📄️ 3.8 Introduction to Medical Imaging EDA
Medical imaging plays a crucial role in modern healthcare, enabling clinicians to visualize and diagnose various medical conditions. Exploratory Data Analysis (EDA) applied to medical images involves extracting insights and patterns from complex visual data. In this section, we'll explore the fundamental concepts and techniques used in medical imaging EDA.
📄️ Resources for Furhter Exploration
- EDA with Pandas