A package for converting time series data from e.g. electronic health records into wide format data.
Project description
Timeseriesflattener
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 | |
|---|---|
| 🚨 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. |
Release history Release notifications | RSS feed
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