Applications of 0-1 Neural Networks in Prescription and Prediction
cs.LG, cs.AI, math.OC, stat.ML
Submitted: 2024-02-29
Updated: 2024-02-29
DOI: 10.1007/s10729-025-09751-5
Code: https://github.com/microsoft/EconML
License: http://creativecommons.org/licenses/by/4.0/
The gist: A key challenge in medical decision making is learning treatment policies for patients with limited observational data.
Terminology
Abstract
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.
Sources
- Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks
- Model Based Reinforcement Learning for Personalized Heparin Dosing
- Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
- Evaluating Robustness of Neural Networks with Mixed Integer Programming
- Solving Mixed Integer Programs Using Neural Networks
- Doubly Robust Policy Evaluation and Learning
- Learning Optimal Prescriptive Trees from Observational Data
- Strong Optimal Classification Trees
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