Neural Information Processing Systems http://nips.cc/

Care coordination and population health management (PHM) are core functions of health systems and community partners, impacting large numbers of Americans enrolled in Medicaid and other safety-net programs. These efforts aim to proactively identify needs, prioritize outreach, and escalate appropriately, all within finite staffing and budget constraints. While outreach modalities (text, phone, video, in-person) carry low clinical risk, their time and opportunity costs vary significantly, making efficiency a primary design goal. In practice, the central operational question is when to deploy expensive in-person outreach versus efficient virtual modalities to maximize value and equity under capacity constraints. These decisions must be made in strictly offline settings, where policies are learned from logged data without exploration at deployment [1]. Classical approaches include constrained Markov decision processes [2], risk-sensitive objectives, and conservative offline RL (e.g., CQL/IQL) [3, 4]. Conformal prediction can provide calibrated error control [5, 6]; ensembles provide practical uncertainty quantification [7]; and decision-time computation is common in control [8]. In health services research and health economic evaluation, cost-effectiveness and cost-benefit analyses (CEA/CBA) guide program-level choices [9-12], but they are not designed for per-patient, per-decision recommendations that adapt to granular state features and logged behavior constraints. 1

Many applications require learning classifiers or regressors that are both accurate and cheap to evaluate. Prediction cost can be drastically reduced if the learned predictor is constructed such that on the majority of the inputs, it uses cheap features and fast evaluations. The main challenge is to do so with little loss in accuracy. In this work we propose a budget-aware strategy based on deep boosted regression trees. In contrast to previous approaches to learning with cost penalties, our method can grow very deep trees that on average are nonetheless cheap to compute. We evaluate our method on a number of datasets and find that it outperforms the current state of the art by a large margin. Our algorithm is easy to implement and its learning time is comparable to that of the original gradient boosting.

Many applications require learning classifiers or regressors that are both accurate and cheap to evaluate. Prediction cost can be drastically reduced if the learned predictor is constructed such that on the majority of the inputs, it uses cheap features and fast evaluations. The main challenge is to do so with little loss in accuracy. In this work we propose a budget-aware strategy based on deep boosted regression trees. In contrast to previous approaches to learning with cost penalties, our method can grow very deep trees that on average are nonetheless cheap to compute. We evaluate our method on a number of datasets and find that it outperforms the current state of the art by a large margin. Our algorithm is easy to implement and its learning time is comparable to that of the original gradient boosting.