PNN模型融合优化与深度学习，用多模态数据给出可优化结果的治疗建议。
Multimodal Prescriptive Deep Learning
- 构建前馈神经网络，从嵌入表示中学习最优治疗处方。
- 在主动脉瓣置换术中降低32%术后并发症，肝外伤中降低超40%死亡率。
- 支持可解释性重构，适合医疗等需可信决策的场景。
我们提出一种多模态深度学习框架——预设神经网络（Prescriptive Neural Networks, PNN），结合优化与机器学习思想，据我们所知是首个处理多模态数据的预设方法。PNN 是一个前馈神经网络，基于嵌入表示训练，输出能优化结果的处方。在两个真实世界的多模态数据集上，PNN 提出的治疗方案显著改善了经导管主动脉瓣置换（TAVR）手术的估计结果，使术后并发症率降低32%；在肝创伤损伤数据中，死亡率降低超过40%。在四个真实世界单模态表格数据集上，PNN 性能优于或至少相当于其他先进预设模型；尤为重要的是，通过知识蒸馏，我们能将可解释的最优分类树模型拟合到 PNN 的处方上，恢复了关键可解释性。最后，我们的多模态 PNN 模型在随机数据划分下表现出与其他预设方法相当的稳定性，并在各数据集中生成合理处方。
原文摘要 · Abstract (English)
We introduce a multimodal deep learning framework, Prescriptive Neural Networks (PNNs), that combines ideas from optimization and machine learning, and is, to the best of our knowledge, the first prescriptive method to handle multimodal data. The PNN is a feedforward neural network trained on embeddings to output an outcome-optimizing prescription. In two real-world multimodal datasets, we demonstrate that PNNs prescribe treatments that are able to significantly improve estimated outcomes in transcatheter aortic valve replacement (TAVR) procedures by reducing estimated postoperative complication rates by 32% and in liver trauma injuries by reducing estimated mortality rates by over 40%. In four real-world, unimodal tabular datasets, we demonstrate that PNNs outperform or perform comparably to other well-known, state-of-the-art prescriptive models; importantly, on tabular datasets, we also recover interpretability through knowledge distillation, fitting interpretable Optimal Classification Tree models onto the PNN prescriptions as classification targets, which is critical for many real-world applications. Finally, we demonstrate that our multimodal PNN models achieve stability across randomized data splits comparable to other prescriptive methods and produce realistic prescriptions across the different datasets.
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