ArXiv Preprint
To what extent can the patient's length of stay in a hospital be predicted
using only an X-ray image? We answer this question by comparing the performance
of machine learning survival models on a novel multi-modal dataset created from
1235 images with textual radiology reports annotated by humans. Although
black-box models predict better on average than interpretable ones, like Cox
proportional hazards, they are not inherently understandable. To overcome this
trust issue, we introduce time-dependent model explanations into the human-AI
decision making process. Explaining models built on both: human-annotated and
algorithm-extracted radiomics features provides valuable insights for
physicians working in a hospital. We believe the presented approach to be
general and widely applicable to other time-to-event medical use cases. For
reproducibility, we open-source code and the TLOS dataset at
https://github.com/mi2datalab/xlungs-trustworthy-los-prediction.
Hubert Baniecki, Bartlomiej Sobieski, Przemysław Bombiński, Patryk Szatkowski, Przemysław Biecek
2023-03-17
