Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation
This work addresses data efficiency in sequential medical decision-making, such as for common bile duct stone evaluation, though it appears incremental as it builds on existing models with consistency assumptions.
The paper tackles the problem of inefficient data collection in multistage healthcare diagnosis by developing an active learning-based method that sequentially gathers only necessary patient data, improving estimation efficiency by 62% to 1838% compared to baseline methods.
Multistage sequential decision-making scenarios are commonly seen in the healthcare diagnosis process. In this paper, an active learning-based method is developed to actively collect only the necessary patient data in a sequential manner. There are two novelties in the proposed method. First, unlike the existing ordinal logistic regression model which only models a single stage, we estimate the parameters for all stages together. Second, it is assumed that the coefficients for common features in different stages are kept consistent. The effectiveness of the proposed method is validated in both a simulation study and a real case study. Compared with the baseline method where the data is modeled individually and independently, the proposed method improves the estimation efficiency by 62\%-1838\%. For both simulation and testing cohorts, the proposed method is more effective, stable, interpretable, and computationally efficient on parameter estimation. The proposed method can be easily extended to a variety of scenarios where decision-making can be done sequentially with only necessary information.