📄️ 1.1 Health Data Sources
In the dynamic landscape of health-related data, a multitude of data sources have arisen. Each offering a unique lens into the realm of healthcare from a different perspective:
📄️ 1.2 Data Licensing & Ethics in Health Informatics
Protecting patient privacy is paramount in the realm of health informatics. While patients contribute a wealth of data to the healthcare ecosystem, it's important to recognize that actual ownership of their data is often limited, particularly when it comes to data generated within Electronic Health Records (EHRs). The complex interplay between patient rights, data ownership, and the healthcare providers' roles underscores the intricate landscape of data licensing.
📄️ 1.3 Data Provenance
Ensuring traceability of patient data is crucial for maintaining its integrity and validity, especially when decisions based on this data can impact patient care.
📄️ 1.4 Data Integrity & Validation in Health
Clean and consistent data is the backbone of effective patient care and groundbreaking research. Validating health data ensures its reliability and accuracy.
📄️ 1.5 Data Acquisition in Healthcare
Diverse data sources in healthcare often come with diverse challenges:
📄️ 1.6 Jupyter Notebooks Introduction
In the realm of health informatics, Jupyter Notebooks have emerged as powerful tools for data exploration, analysis, and communication. Originally developed in 2011 as part of the IPython project, Jupyter Notebooks have gained widespread popularity due to their interactive and versatile nature.
📄️ 1.7 Google Colab: The Cloud-Based Jupyter Notebook
Python can be a pain in the butt to install locally on your own machine, which means jupyter notebook, which can be installed with a simple pip install notebook or pip install jupyterlab (official documentation found here for installation) can still be difficult dispite it being a simple command to execute in your termal. As an example, depending on if you are on a Mac (M-chip or intel) or PC...there are many difficulities that can arise and error messages that would need to be addressed during the installation process.
📄️ 1.8 Jupyter Notebooks: How They Work
Jupyter Notebooks have revolutionized the way data analysis and exploration are conducted. These interactive documents combine code, visualizations, and explanatory text, allowing seamless collaboration and knowledge sharing among health informaticists. Let's delve into the technical details of how Jupyter Notebooks work and why they're an essential tool in your health informatics journey.
📄️ 1.9 General Data Formats
Health informatics deals with a multitude of data formats tailored to specific needs. Let's explore some common data formats, their use cases, and structures:
📄️ 1.10 Intro to pandas for Importing Data
Pandas, a powerful Python data manipulation library, is an indispensable tool for working with health data due to its versatility and ease of use. Whether you're dealing with electronic health records (EHRs), medical imaging data, clinical trial results, or any other health-related dataset, pandas provides the tools to efficiently organize, analyze, and visualize the data.
📄️ 1.11 Native Python File Handling for Importing Data
Native Python offers a versatile set of tools for extracting, processing, and manipulating raw health data, allowing you to address unique challenges often encountered in healthcare datasets.
📄️ 1.12 Important Python Concepts and Methods
Before we move in, we need to discuss and introduce some common python methods that will be utilized in this book.
📄️ Resources for Further Exploration
Reading and Tutorials
📄️ End of Chapter Exercises
Introduction to Jupyter Notebooks (Google Colab) and Data Acquisition