This repository holds code for the NHSX Analytics Unit PhD internship project (previously known as Synthetic Data Generation - Longitudinal) demonstrating the use of SynPath version 1.0.0 for creating a diabetes pathway by Tiyi Morris. This repo sits as a module inside the templates folder https://github.com/nhsx/SynPath/tree/master/template as t2dm @ #version#.

See Readme in https://github.com/nhsx/SynPath for SynPath documentation.

Project Description - Synthetic Data Exploration: Longitudinal

Note: No data, public or private are shared in this repository.

• The main code is found in the `t2dm' folder of the repository
• The accompanying [report](./reports/Technical Report (SynPath Diabetes) v1.pdf) is also available in the reports folder

This model is meant to show a simulation of patients in the NHS through the type 2 diabetes pathway.

Our model takes place in a fictional local area with two hospitals providing outpatient and inpatient services, and five GP practices.

Patient records will be constructed from scratch using a Bayesian network. Patients and environments will be added to the model using the JSON format.

The model will run for a year with the first 500 patients and then the sensitivity of the initial set up (decisions made in each setting and their outcomes) will be evaluated. This is so the patients are in care settings that are relevant to their condition and it’s severity.

After the first year that the model runs a further 9,500 patients will be added with the optimisation learning turned on with the output being recorded.

Patients will return to GP care if they develop end-stage kidney disease or liver disease.

Dictionaries are used to input patients through patient_infos.json.

Future versions of the model could use existing data sets (e.g. CPRD) to create a more accurate representation of 10,000 patients.

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

See CONTRIBUTING.md for detailed guidance.

Distributed under the MIT License. See LICENSE for more information.

To find out more about the Analytics Unit visit our project website or get in touch at [email protected].