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Indexing the Event Calculus: Towards practical human-readable Personal Health Systems.

Nicola Falcionelli1, Paolo Sernani1, Albert Brugués2

  • 1Università Politecnica delle Marche, Ancona, Italy.

Artificial Intelligence in Medicine
|November 17, 2018
PubMed
Summary
This summary is machine-generated.

Personal Health Systems (PHS) enable remote monitoring of chronic diseases by processing large patient data streams. This study introduces an Event Calculus framework with novel indexing techniques for efficient data analysis and improved patient care.

Keywords:
Event calculusKnowledge representationLogic programmingMulti-agent systemsPersonal health SystemsPrologReasoning

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Personal Health Systems (PHS) generate vast amounts of patient data, particularly for chronic non-communicable diseases like Type 1 Diabetes.
  • Current PHS struggle with efficient data indexing and querying of physiological time series, hindering accurate patient monitoring by medical professionals.
  • Integrating domain expertise into PHS is crucial for effective clinical decision-making, but requires adaptable knowledge representation.

Purpose of the Study:

  • To develop a standardized, logic programming-based framework for expressing and utilizing domain knowledge in PHS.
  • To propose and evaluate novel indexing mechanisms for enhancing the performance of Event Calculus reasoning in PHS.
  • To compare the efficiency of different tree-based indexing structures (k-d trees, interval trees, red-black trees) for real-time physiological data analysis.

Main Methods:

  • An Event Calculus-based reasoning framework was developed to standardize domain knowledge into monitoring rules.
  • Three tree indexing structures (k-d trees, interval trees, red-black trees) were implemented and integrated into the Event Calculus Knowledge Base.
  • Performance analysis involved measuring rule checking times with increasing event data volumes and varying inter-arrival times.
  • The Multi-Agent paradigm was employed using the MAGPIE platform to integrate reasoning engines and sensors.

Main Results:

  • The proposed Event Calculus framework effectively standardizes domain knowledge for PHS applications.
  • Customized jREC demonstrated superior performance with high-frequency data (short inter-arrival times).
  • Standard Event Calculus with k-d trees proved more efficient for sparse data (longer inter-arrival times).
  • The Multi-Agent system successfully integrated reasoning and sensing components.

Conclusions:

  • Logic programming, specifically Event Calculus, offers a robust approach for integrating expert knowledge into PHS.
  • Optimized indexing strategies are essential for improving the real-time performance of PHS, with method selection dependent on data characteristics.
  • The developed framework and indexing techniques enhance the capability of PHS for efficient chronic disease monitoring.