  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.
