A package for converting time series data from e.g. electronic health records into wide format data.
Project description
Time-series Flattener
Roadmap
Roadmap is tracked on our kanban board.
🔧 Installation
To get started using timeseriesflattener simply install it using pip by running the following line in your terminal:
pip install timeseriesflattener
📖 Documentation
Documentation | |
---|---|
🎛 API References | The detailed reference for timeseriesflattener's API. Including function documentation |
🙋 FAQ | Frequently asked question |
💬 Where to ask questions
Type | |
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🚨 Bug Reports | GitHub Issue Tracker |
🎁 Feature Requests & Ideas | GitHub Issue Tracker |
👩💻 Usage Questions | GitHub Discussions |
🗯 General Discussion | GitHub Discussions |
🎓 Projects
PSYCOP projects which use timeseriesflattener
. Note that some of these projects have yet to be published and are thus private.
Project | Publications | |
---|---|---|
Type 2 Diabetes | Prediction of type 2 diabetes among patients with visits to psychiatric hospital departments | |
Cancer | Prediction of Cancer among patients with visits to psychiatric hospital departments | |
COPD | Prediction of Chronic obstructive pulmonary disease (COPD) among patients with visits to psychiatric hospital departments | |
Forced admissions | Prediction of forced admissions of patients to the psychiatric hospital departments. Encompasses two seperate projects: 1. Prediciting at time of discharge for inpatient admissions. 2. Predicting day before outpatient admissions. | |
Coersion | Prediction of coercion among patients admittied to the hospital psychiatric department. Encompasses predicting mechanical restraint, sedative medication and manual restraint 48 hours before coercion occurs. |
Project details
Release history Release notifications | RSS feed
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