Deep and interpretable regression models for ordinal outcomes

作者：

Highlights：

• We present interpretable ordinal DL models incorporating image and tabular data.

• ONTRAMs are interpretable and achieve on-par performance with common DL models.

• Model components possess a direct statistical interpretation on the odds scale.

• ONTRAMs show a higher training efficiency for ordinal outcomes than softmax DL models.

摘要

•We present interpretable ordinal DL models incorporating image and tabular data.•ONTRAMs are interpretable and achieve on-par performance with common DL models.•Model components possess a direct statistical interpretation on the odds scale.•ONTRAMs show a higher training efficiency for ordinal outcomes than softmax DL models.

论文关键词：Deep learning,Interpretability,Distributional regression,Ordinal regression,Transformation models

论文评审过程：Received 15 December 2020, Revised 12 August 2021, Accepted 18 August 2021, Available online 19 August 2021, Version of Record 15 September 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108263