Service Selection using Predictive Models and Monte-Carlo Tree Search
  This article proposes a method for automated service selection to improve
treatment efficacy and reduce re-hospitalization costs. A predictive model is
developed using the National Home and Hospice Care Survey (NHHCS) dataset to
quantify the effect of care services on the risk of re-hospitalization. By
taking the patient's characteristics and other selected services into account,
the model is able to indicate the overall effectiveness of a combination of
services for a specific NHHCS patient. The developed model is incorporated in
Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services
that minimize the risk of emergency re-hospitalization. MCTS serves as a risk
minimization algorithm in this case, using the predictive model for guidance
during the search. Using this method on the NHHCS dataset, a significant
reduction in risk of re-hospitalization is observed compared to the original
selections made by clinicians. An 11.89 percentage points risk reduction is
achieved on average. Higher reductions of roughly 40 percentage points on
average are observed for NHHCS patients in the highest risk categories. These
results seem to indicate that there is enormous potential for improving service
selection in the near future.
