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. 2021 Dec 3;23(12):e29812.
doi: 10.2196/29812.

Analyzing Patient Trajectories With Artificial Intelligence

Ahmed Allam  1   2 , Stefan Feuerriegel  3   4   5 , Michael Rebhan  3 , Michael Krauthammer  1   2   6
Affiliations

Analyzing Patient Trajectories With Artificial Intelligence

Ahmed Allam et al. J Med Internet Res. .

Abstract

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

Keywords: artificial intelligence; digital medicine; longitudinal data; machine learning; patient trajectories.

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Conflict of interest statement

Conflicts of Interest: MR declares employment with the Novartis Institutes for Biomedical Research, Switzerland.

Figures

Figure 1
Figure 1
Analyzing patient trajectories with artificial intelligence in digital medicine.
Figure 2
Figure 2
Example of artificial intelligence–based trajectory analysis. RNN: recurrent neural network.
Figure 3
Figure 3
Difference between noninformative and informative sampling.
Figure 4
Figure 4
Example of a hidden Markov model. HbA1c: hemoglobin A1c.
See this image and copyright information in PMC

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