A key challenge in medical decision making is learning treatment policies for
patients with limited observational data. This challenge is particularly
evident in personalized healthcare decision-making, where models need to take
into account the intricate relationships between patient characteristics,
treatment options, and health outcomes. To address this, we introduce
prescriptive networks (PNNs), shallow 0-1 neural networks trained with mixed
integer programming that can be used with counterfactual estimation to optimize
policies in medium data settings. These models offer greater interpretability
than deep neural networks and can encode more complex policies than common
models such as decision trees. We show that PNNs can outperform existing
methods in both synthetic data experiments and in a case study of assigning
treatments for postpartum hypertension. In particular, PNNs are shown to
produce policies that could reduce peak blood pressure by 5.47 mm Hg (p=0.02)
over existing clinical practice, and by 2 mm Hg (p=0.01) over the next best
prescriptive modeling technique. Moreover PNNs were more likely than all other
models to correctly identify clinically significant features while existing
models relied on potentially dangerous features such as patient insurance
information and race that could lead to bias in treatment.

医学决策制定中的一个关键挑战是学习对具有有限观察数据的患者进行治疗政策。本研究引入了预测网络（PNNs），使用混合整数规划进行训练的浅层 0-1 神经网络，可与反事实估计一起在中等数据环境中优化政策。通过合成数据实验和产后高血压治疗案例研究，证明 PNNs 可以在性能上超越现有方法，并能够输出具有临床意义的治疗政策。