Timely Decision Making with Practical Consideration

Abstract: Biomarker levels are associated with adverse events among patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients has the potential to improve patient outcomes while reducing healthcare costs. From the perspective of sequential decision making, we propose a novel approach for early classification of time series incorporating various constraints. The classifier either concludes positively/negatively based on the series or waits for further information from the next time step. We characterize the trade-off among multiple criteria, such as sensitivity, specificity and earliness. We also explicitly formulate the optimal solution, which is tractable via plugging-in estimators. Experimental studies demonstrate its potential in real-world applications.