Bolt, H. 2023. ehr-lshtm/acute_kidney_injury_seasonality_ML. [Online]. GitHub. Available from: https://github.com/ehr-lshtm/acute_kidney_injury_seasonality_ML
Bolt, H. ehr-lshtm/acute_kidney_injury_seasonality_ML [Internet]. GitHub; 2023. Available from: https://github.com/ehr-lshtm/acute_kidney_injury_seasonality_ML
Bolt, H (2023). ehr-lshtm/acute_kidney_injury_seasonality_ML. [Data Collection]. GitHub. https://github.com/ehr-lshtm/acute_kidney_injury_seasonality_ML
Description
This is the project code for the paper 'Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records'. This contains only non-disclosive files, that is, code without file paths, and summary statistics. This template is set up so only files that are safe to upload to Github, such as code, are uploaded by default. The underlying data used for this study was from the Clinical Practice Research Datalink, which includes UK Primary Care Data, and linked data such as Hospital Episode Statistics. Access to this data is subject to protocol approval via CPRD’s Research Data Governance (RDG) Process. Given the sensitive nature of these data, this is not uploaded to the repository. Access to data is available on request to the Clinical Practice Research Datalink (https://cprd.com/how-access-cprd-data).
Keywords
Data capture method | Unknown |
---|---|
Date (Date published in a 3rd party system) | 9 May 2023 |
Language(s) of written materials | English |
Data Creators | Bolt, H |
---|---|
LSHTM Faculty/Department | Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
Funders |
|
---|
Date Deposited | 17 Aug 2023 12:08 |
---|---|
Last Modified | 17 Aug 2023 12:08 |
Publisher | GitHub |