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.

翻译：仅凭一张X光片能在多大程度上预测患者住院时长？我们通过构建包含1235张图像及人工标注文本报告的新型多模态数据集，对比机器学习生存模型性能来回答这一问题。尽管黑箱模型的平均预测效果优于Cox比例风险等可解释模型，但其本质缺乏可理解性。为克服这一信任难题，我们将时变模型解释引入人机决策流程。对基于人工标注与算法提取的放射组学特征构建的模型进行解释，能为医院临床医生提供重要见解。我们认为该方法具有普适性，可广泛适用于其他时间事件型医疗场景。为保障可复现性，我们已在https://github.com/mi2datalab/xlungs-trustworthy-los-prediction开源代码与TLOS数据集。