https://github.com/hmelberg/health-analytics-using-python

A set of notebooks demonstrating methods for how to analyze hospital and prescription data using Python
https://github.com/hmelberg/health-analytics-using-python

Last synced: about 19 hours ago
JSON representation

A set of notebooks demonstrating methods for how to analyze hospital and prescription data using Python

• Host: GitHub
• URL: https://github.com/hmelberg/health-analytics-using-python
• Owner: hmelberg
• Created: 2019-11-27T05:24:39.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2024-03-20T17:22:00.000Z (8 months ago)
• Last Synced: 2024-03-20T18:48:20.475Z (8 months ago)
• Language: Jupyter Notebook
• Size: 2.2 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Health analytics using Python and Pandas

A set of notebooks demonstrating methods for how to analyze hospital and prescription data using Python

**Content**

[1. Appetizers: Some examples](https://github.com/hmelberg/health-analytics-using-python/blob/master/1_Appetizers_Some_examples.ipynb)

[2. Counting: How many and how often](https://github.com/hmelberg/health-analytics-using-python/blob/master/2_Ways_of_counting_How_many_and_how_often.ipynb)

[3. Medical codes and Notation I: A case study using Charlsons Comorbidity Index](https://github.com/hmelberg/health-analytics-using-python/blob/master/Notation_to_deal_with_medical_codes_and_Charlsons_comorbidity_index.ipynb)

[4. Medical codes: From codes to labels](https://github.com/hmelberg/health-analytics-using-python/blob/master/Medical_codesv2.ipynb)

[5. Organizing your data: The answer is half long](https://github.com/hmelberg/health-analytics-using-python/blob/master/4_Organizing_your_data_The_answer_is_half_long.ipynb)

[6. Typical Treatment: What is it, and how to measure it?](

https://github.com/hmelberg/health-analytics-using-python/blob/master/5_Typical_Treatment_Part_II.ipynb)

[7. Measuring the effect of an intervention: Elimination of copayment and diff-in-diff]( https://github.com/hmelberg/health-analytics-using-python/blob/master/gp_visits.ipynb)