indicpy4health
Lightweight, Fast, and Intuitive Indicator Calculations Python Library from Health data.
Science Score: 49.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
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○Academic email domains
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○Scientific vocabulary similarity
Low similarity (11.9%) to scientific vocabulary
Keywords
Repository
Lightweight, Fast, and Intuitive Indicator Calculations Python Library from Health data.
Basic Info
- Host: GitHub
- Owner: cienciadedatosysalud
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://cienciadedatosysalud.github.io/IndicPy4Health/
- Size: 2.24 MB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 2
Topics
Metadata Files
README.md
IndicPy4Health
IndicPy4Health is a Lightweight, Fast, and Intuitive Indicator Calculations Python Library from Health data.
Example
``` py import pandas as pd from indicpy4health import RuleEngine, MatchAnyWhere, run_indicators
hospdataframe = pd.DataFrame({ "episodeid": [1, 2, 3], "age": [45, 60, 32], "diagnosis1": ["F10.10", "I20", "I60"], "diagnosis2": ["E11", "J45", "I25"], "diagnosis3": ["I60", "K35", "F10.120"], "presentonadmissiond1": [False, False, False], "presentonadmissiond2": [False, True, False], "presentonadmission_d3": [False, True, True], })
reng = RuleEngine(hospdataframe, "episodeid")
target_columns = ["diagnosis2", "diagnosis3"]
definition_codes = ["F10.10", "F10.11", "F10.120", "F10.121"]
filtercolumns = ["presentonadmissiond2", "presentonadmission_d3"]
lookup_values = ["true"]
alcoholindicatorpoa = MatchAnyWhere( reng, "alcoholipoa", targetcolumns, definitioncodes, filtercolumns, lookupvalues )
alcoholiregexpoa = MatchAnyWhere( reng, "alcoholiregexpoa", targetcolumns, ["F10"], filtercolumns, lookupvalues, regexprefix_search=True )
Include the indicators in a list and apply them
indicatorslist = [alcoholindicatorpoa, alcoholiregexpoa]
runindicators(self.reng, indicatorslist, appendresults=False, csvpath="./results.csv" )
```
IndicatorBuilder
Effortlessly generate indicator calculation script templates using 'IndicatorBuilder'. This web tool reads a CSV file, where each column represents an indicator by including its respective definition codes, and outputs a script template ready to be copied and used in R or Python. You can streamline your data analysis workflow with 'IndicatorBuilder'.
The use of 'IndicatorBuilder' complements the Python library IndicPy4Health and the R package IndicR4Health, providing an easy-to-use tool scripting the definition of any indicator within your preferred programming environment.
📜 Disclaimer
This software is provided "as is," without any warranties of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, and non-infringement.
In no event shall the authors, contributors, or maintainers be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including but not limited to loss of use, data, or profits), regardless of the cause and under any liability theory, whether in contract, strict liability, or tort (including negligence or any other cause), arising in any way from the use of this software, even if advised of the possibility of such damages.
The user assumes full responsibility for the use of this library, including evaluating its suitability and safety in the context of their application.
Owner
- Login: cienciadedatosysalud
- Kind: user
- Location: España
- Company: Instituto Aragonés de Ciencias de la Salud
- Website: https://cienciadedatosysalud.org/
- Twitter: atlasvpm
- Repositories: 1
- Profile: https://github.com/cienciadedatosysalud
We are the Data Science for Health Services and Policy Research of the Instituto Aragonés de Ciencias de la Salud [Aragon Institute of Health Sciences].
GitHub Events
Total
- Issues event: 4
- Push event: 3
Last Year
- Issues event: 4
- Push event: 3
Dependencies
- actions/checkout v3 composite
- actions/setup-python v4 composite
- peaceiris/actions-gh-pages v3 composite
- pydata-sphinx-theme >=0.8.1
- sphinx >=6.0
- sphinx-autodoc-typehints *
- sphinx-rtd-theme *
- sphinxcontrib-napoleon *
- duckdb 1.2.2
- numpy 2.0.2
- numpy 2.2.5
- pandas 2.2.3
- python-dateutil 2.9.0.post0
- pytz 2025.2
- six 1.17.0
- tzdata 2025.2
- duckdb ^1.2.2
- pandas ^2.2.3
- python ^3.9