• DocumentCode
    243804
  • Title

    Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways

  • Author

    Rui Liu ; Srinivasan, Raj Velamur ; Zolfaghar, Kiyana ; Si-Chi Chin ; Roy, Senjuti Basu ; Hasan, Aftab ; Hazel, David

  • Author_Institution
    Center for Data Sci., UW Tacoma, Tacoma, WA, USA
  • fYear
    2014
  • fDate
    14-14 Dec. 2014
  • Firstpage
    1219
  • Lastpage
    1222
  • Abstract
    Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increased attention in order to augment the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to visually explore and discover clinical pathways. The interactive web service efficiently collects and displays patient information in a meaningful way to support an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. To support real-time recommendation and visualization, a key-value structure has been implemented to traverse the Bayesian Network faster. Additionally, Pathway-Finder is a cloud based web service hosted on Microsoft Azure which enables the health providers to access the system without the need to deploy analytics infrastructure. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).
  • Keywords
    Web services; belief networks; cloud computing; data visualisation; medical information systems; patient care; patient treatment; recommender systems; resource allocation; Bayesian network; Microsoft Azure; Pathway-Finder; causal relationship; clinical pathway analysis; cloud based Web service; complex care process; health provider; heart failure; interactive Web service; interactive recommender system; patient information; patient outcome; patient treatment process; personalized care pathway; personalized treatment plan; real-time recommendation; real-time visualization; resource allocation; Algorithm design and analysis; Bayes methods; Diseases; Heart; Medical treatment; Probability distribution; Real-time systems; bayesian network; heart failure; intervention recommendation; risk of readmission;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
  • Conference_Location
    Shenzhen
  • Print_ISBN
    978-1-4799-4275-6
  • Type

    conf

  • DOI
    10.1109/ICDMW.2014.37
  • Filename
    7022738