Careful data curation and analyses are essential in developing machine learning algorithms, that may usefully contribute to solving problems encountered in routine healthcare. Nevertheless, many valuable contributions never transition from the computer to the bedside. Often implementation is never attempted, or they fail to get the relevant CE marking (or equivalent local standard), or their implementation fails to elicit the intended health benefit (failure due to lack of clinical utility).
The current courses focus on the latter and provides the foundation necessary to plan and conduct clinical evaluation of machine learning solutions to fairly assess their contribution to clinical practice. Specifically, using relevant case studies you will learn:
- A short introduction on prognostication and machine learning
- Field usability and feasibility analyses
- Early clinical evaluation
- Introduction to causal inference
- Limitations of traditional RCTs and alternative designs for clinical evaluations
- Critical considerations of front-end-development.
Check the DATAclinic Animation Video here.
This online course was created in partnership with the University Medical Center Utrecht as part of the DATAclinic Erasmus+ project.
Lecturers
Dr. Floriaan Schmidt
Target audience
Healthcare professionals
Aim of the course
More information will follow soon
Study load
- 20-40 hours
- Self-paced - 100% flexible
- Study online anytime, anywhere
- English
Costs
More information will follow soon on the DATAclinic website.
Contact details
Enroll for this online course via the DATAclinic website.
If you want to learn more about the DATAclinic programme, partners and content, we direct you to the DATAclinic website. Do you have any questions about the contents, or do you require more information about the DATAclinic programme? Feel free to contact info@elevatehealth.eu.
The DATAclinic project will benefit medical research and patient care in Europe by improving knowledge and expertise of healthcare professionals to effectively collaborate and lead in an ethical, legally correct, secure way, data science activities and projects. Industrial partners are instrumental for healthcare professionals to further understand and appreciate the relevance of academic-industry collaborations to leverage existing healthcare data science and improve patient care.