Analyzing Patient Trajectories With Artificial Intelligence

• DOI: 10.2196/29812
• Publication Date: 2021-12-03T15:15:50Z
• Authors: Ahmed Allam; Stefan Feuerriegel; Michael Rebhan; Michael Krauthammer
• Keywords: longitudinal data; digital medicine; Computer applications to medicine. Medical informatics; R858-859.7; 610 Medicine & health; Health Informatics; artificial intelligence; 3. Good health; Viewpoint; machine learning; Artificial Intelligence; Humans; Public aspects of medicine; RA1-1270; 11493 Department of Quantitative Biomedicine; patient trajectories; longitudinal data; digital medicine; artificial intelligence; machine learning; patient trajectories; 2718 Health Informatics

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.