NOCTA: Non-Greedy Objective Cost-Tradeoff Acquisition for Longitudinal Data
This addresses resource-limited prediction in domains like healthcare, where costs and risks matter, but it appears incremental as it builds on existing acquisition methods with temporal and cost considerations.
The paper tackled the problem of sequentially acquiring informative features under resource constraints in temporal prediction tasks, such as healthcare, by proposing NOCTA, a non-greedy method that accounts for cost and dynamics, and demonstrated that its variants outperform existing baselines on synthetic and real-world medical datasets.
In many critical applications, resource constraints limit the amount of information that can be gathered to make predictions. For example, in healthcare, patient data often spans diverse features ranging from lab tests to imaging studies. Each feature may carry different information and must be acquired at a respective cost of time, money, or risk to the patient. Moreover, temporal prediction tasks, where both instance features and labels evolve over time, introduce additional complexity in deciding when or what information is important. In this work, we propose NOCTA, a Non-Greedy Objective Cost-Tradeoff Acquisition method that sequentially acquires the most informative features at inference time while accounting for both temporal dynamics and acquisition cost. We first introduce a cohesive estimation target for our NOCTA setting, and then develop two complementary estimators: 1) a non-parametric method based on nearest neighbors to guide the acquisition (NOCTA-NP), and 2) a parametric method that directly predicts the utility of potential acquisitions (NOCTA-P). Experiments on synthetic and real-world medical datasets demonstrate that both NOCTA variants outperform existing baselines.