Safe AI for health and beyond -- Monitoring to transform a health service
| Autor: | Abroshan, Mahed, Burkhart, Michael, Giles, Oscar, Greenbury, Sam, Kourtzi, Zoe, Roberts, Jack, van der Schaar, Mihaela, Steyn, Jannetta S, Wilson, Alan, Yong, May |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems often stop at the point of publication, proof of concept or when made accessible through some mode of deployment. However, a model in the medical domain risks becoming obsolete as patient demographics, systems and clinical practices change. The maintenance and monitoring of predictive model performance post-publication is crucial to enable their safe and effective long-term use. We will assess the infrastructure required to monitor the outputs of a machine learning algorithm, and present two scenarios with examples of monitoring and updates of models, firstly on a breast cancer prognosis model trained on public longitudinal data, and secondly on a neurodegenerative stratification algorithm that is currently being developed and tested in clinic. Comment: 12 pages, 3 figures |
| Databáze: | arXiv |
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