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 | |
|---|---|
| 🚨 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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file timeseriesflattener-0.17.0.tar.gz.
File metadata
- Download URL: timeseriesflattener-0.17.0.tar.gz
- Upload date:
- Size: 24.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.9.2 readme-renderer/37.3 requests/2.28.1 requests-toolbelt/0.10.1 urllib3/1.26.13 tqdm/4.64.1 importlib-metadata/5.1.0 keyring/23.11.0 rfc3986/2.0.0 colorama/0.4.6 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 62b6dd015583eb7a79bd4ac153777860091c996f72e257426fce4485c4e82ac2 |
|
| MD5 | e1232706606fd3712190e70bf0e64dd8 |
|
| BLAKE2b-256 | f0d2c6fce8f182ce3609c7a8688b074b8e7c6fa36b2002c95a278cb0d0ebb348 |
File details
Details for the file timeseriesflattener-0.17.0-py3-none-any.whl.
File metadata
- Download URL: timeseriesflattener-0.17.0-py3-none-any.whl
- Upload date:
- Size: 27.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.9.2 readme-renderer/37.3 requests/2.28.1 requests-toolbelt/0.10.1 urllib3/1.26.13 tqdm/4.64.1 importlib-metadata/5.1.0 keyring/23.11.0 rfc3986/2.0.0 colorama/0.4.6 CPython/3.9.16
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8d0f75c657d81d81be597bcb3eaa69fb9dbc98fe84505951a3ac63a2c7aa1a2f |
|
| MD5 | c654654e07526f7c9f48748cbeae3a42 |
|
| BLAKE2b-256 | 383b9ab54b66cb1d20afed2ba62b391bab21f6b0949e580d96aa9f9b36d76348 |
