Cloud-based Smart CDSS for chronic diseases

Maqbool Hussain, Asad Masood Khattak, Wajahat Ali Khan, Iram Fatima, Muhammad Bilal Amin, Zeeshan Pervez, Rabia Batool, Muhammad Amir Saleem, Muhammad Afzal, Muhammad Faheem, Muhammad Hameed Saddiqi, Sungyoung Lee. Lee, Khalid Latif

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)

Abstract

The rise in living standards that has occurred with the advancement of new technologies has increased the demand for sophisticated standards-based health-care applications that provide services anytime, anywhere, and with low cost. To achieve this objective, we have designed and developed the Smart Clinical Decision Support System (Smart CDSS) that takes input from diverse modalities, such as sensors, user profile information, social media, clinical knowledge bases, and medical experts to generate standards-based personalized recommendations. Smartphone-based, accelerometer-based, environment-based activity-recognition algorithms are developed with this system that recognizes users’ daily life activities. For example, social media data are captured for a diabetic patient from his/her social interactions on Twitter, e-mail, and Trajectory and then combined with clinical observations from real encounters in health-care facilities. The input is converted into standard interface following HL7 vMR standards and submitted to the Smart CDSS for it to generate recommendations. We tested the system for 100 patients from Saint Mary’s Hospital: 20 with type-1 diabetes, 40 with type-2 diabetes mellitus, and 40 with suspicions for diabetes but no diagnosis during clinical observations. The system knowledge base was initialized with standard guidelines from online resources for diabetes, represented in HL7 Arden syntax. The system generates recommendations based on physicians’ guidelines provided at the hospital during patient follow-ups. With support from the Azure cloud infrastructure, the system executed the set of guidelines represented in Arden syntax in a reasonable amount of time. Scheduling and executing the 3–5 guidelines called medical logic modules (MLMs) required less than a second.
Original languageEnglish
Pages (from-to)153–175
Number of pages23
JournalHealth and Technology
Volume3
Issue number2
DOIs
Publication statusPublished - 27 Mar 2013
Externally publishedYes

Keywords

  • activity recognitiom
  • social media
  • cloud computing
  • healthcare
  • CDSS
  • HL7

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