Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation

TL;DR

This paper introduces an active learning-based multistage decision-making model tailored for healthcare diagnosis, efficiently collecting necessary patient data and improving estimation accuracy across stages.

Contribution

It proposes a novel joint estimation approach for all stages and assumes consistent coefficients for common features, enhancing efficiency over traditional methods.

Findings

Improves estimation efficiency by up to 1838%.

More effective, stable, and interpretable than baseline methods.

Validated in simulation and real case study.

Abstract

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.