Learning Stroke Treatment Progression Models for an MDP Clinical Decision Support System

@inproceedings{Coroian2015LearningST,
  title={Learning Stroke Treatment Progression Models for an MDP Clinical Decision Support System},
  author={Dan C. Coroian and K. Hauser},
  booktitle={SDM},
  year={2015}
}
  • Dan C. Coroian, K. Hauser
  • Published in SDM 2015
  • Computer Science
  • This paper describes a clinical decision support framework in multi-step health care domains that can dynamically recommend optimal treatment plans with respect to both patient outcomes and expected treatment cost. Our system uses a modified POMDP framework in which hidden states are not explicitly modeled, but rather, probabilistic models for predicting future observables given observation and action histories are learned directly from electronic health record (EHR) data. High quality… CONTINUE READING
    Stroke Care and the Role of Big Data in Healthcare and Stroke
    • 2
    • Highly Influenced

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 27 REFERENCES
    Medical decision making for patients with Parkinson disease under Average Cost Criterion
    • 17
    • PDF
    Prognostic Bayesian networks: I: Rationale, learning procedure, and clinical use
    • 53
    Prognostic Bayesian networks: II: An application in the domain of cardiac surgery
    • 28
    • PDF
    Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty
    • 145
    • Highly Influential
    • PDF