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The University of Edinburgh is developing a Clinical Decision Support Tool for diagnosing acute heart attack in the Emergency Department. The research team involved has recently developed and published the research surrounding the machine learning algorithm (XGBoost)(https://www.nature.com/articles/s41591-023-02325-4). An example of the algorithm’s inputs and outputs for educational purposes only are available as a shiny app (https://decision-support.shinyapps.io/code-acs/).
The University seeks to commission external solutions and services for the following tasks:
1. The creation of standalone software which will be fit for UKCA marking as a Class I Medical Device under the current legislation.
2. Consultancy support for the regulatory process, providing systems and expertise to ensure all relevant legislation has been complied with. This will include any steps required for self-certification and device registration.
The objective of this exercise is to obtain up-to-date information and feedback from potential vendors to understand market capabilities.
No supplier information available.
This procurement was divided into 1 lots, each awarded separately.
| Lot | Value | Status |
|---|---|---|
| Lot 1 | - | planned |