Interpretability of Input Representations for Gait Classification in Patients after Total Hip Arthroplasty

Many machine learning models show black box characteristics and, therefore, a lack of transparency, interpretability, and trustworthiness. This strongly limits their practical application in clinical contexts. For overcoming these limitations, Explainable Artificial Intelligence (XAI) has shown prom...

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Detalles Bibliográficos
Autores principales: Dindorf, Carlo, Teufl, Wolfgang, Taetz, Bertram, Bleser, Gabriele, Fröhlich, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471970/
https://www.ncbi.nlm.nih.gov/pubmed/32781583
http://dx.doi.org/10.3390/s20164385

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7471970/
https://www.ncbi.nlm.nih.gov/pubmed/32781583
http://dx.doi.org/10.3390/s20164385

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