Aarhus-Psychiatry-Research/timeseriesflattener
Converting irregularly spaced time series, such as eletronic health records, into dataframes for tabular classification.
This tool helps researchers and data scientists working with patient records to prepare their data for predictive modeling. It takes raw, irregularly sampled time series data, like electronic health records, and transforms them into a structured table. This table includes aggregated predictor values from specific time windows and an outcome value for each prediction time, making it ready for machine learning analysis.
No commits in the last 6 months. Available on PyPI.
Use this if you need to convert complex, irregularly spaced time series data from sources like electronic health records into a 'flattened' table suitable for machine learning predictions.
Not ideal if your data is already uniformly sampled or if you primarily need to analyze raw, high-frequency time series patterns without creating a tabular prediction dataset.
Stars
19
Forks
2
Language
Python
License
MIT
Category
Last pushed
Jun 17, 2025
Commits (30d)
0
Dependencies
15
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