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Ahmed Allam1,2, Stefan Feuerriegel3,4,5, Michael Rebhan3

  • 1Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.

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|December 6, 2021
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Summary
This summary is machine-generated.

Artificial intelligence (AI) can analyze patient trajectories from digital medicine data to predict disease progression. This approach leverages longitudinal health event data for improved personalized risk scoring and disease pathway discovery.

Keywords:
artificial intelligencedigital medicinelongitudinal datamachine learningpatient trajectories

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Area of Science:

  • Digital medicine
  • Artificial intelligence
  • Health informatics

Background:

  • Patient data in digital medicine often form longitudinal trajectories.
  • These trajectories are predictive of disease course and facilitate care.
  • Current methods often use limited data, ignoring trajectory information.

Purpose of the Study:

  • To provide an overview of trajectory-aware AI solutions for digital medicine.
  • To examine AI workflow implications for developing disease models from patient trajectories.
  • To suggest future directions for AI in analyzing longitudinal health data.

Main Methods:

  • Review of recent AI efforts in developing trajectory-aware solutions.
  • Analysis of AI workflow stages: problem definition, data processing, modeling, evaluation, and interpretation.
  • Examination of implications for disease modeling using patient trajectories.

Main Results:

  • Identified a need for new AI solutions to analyze rich longitudinal patient data.
  • Outlined challenges and opportunities in applying AI to patient trajectories.
  • Highlighted the potential of trajectory-aware AI for personalized medicine.

Conclusions:

  • Trajectory-aware AI can build robust models for personalized risk scoring.
  • AI solutions can enable patient subtyping and disease pathway discovery.
  • Future AI development should focus on harnessing the full potential of longitudinal patient data.