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Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction
BACKGROUND: Data drift can negatively impact the performance of machine learning algorithms (MLAs) that were trained on historical data. As such, MLAs should be continuously monitored and tuned to overcome the systematic changes that occur in the distribution of data. In this paper, we study the ext...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Cold Spring Harbor Laboratory
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9196120/ https://www.ncbi.nlm.nih.gov/pubmed/35702157 http://dx.doi.org/10.1101/2022.06.06.22276062 |