arXiv:2111.05385v3 Announce Type: replace Abstract: Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectional BMI recordings has been heavily studied, the role of BMI trajectories is much less explored. In this study, we use a machine-learning approach to subtype individuals' risk of developing 18 major chronic diseases by using their BMI trajectories extracted from a large and geographically diverse EHR dataset capturing the health status of around two million individu
The increasing availability of large, diverse electronic health record (EHR) datasets and advancements in machine learning techniques now allow for sophisticated analysis of longitudinal health patterns.
This research highlights AI's potential to move beyond static health indicators to dynamic, predictive health insights, fundamentally altering how chronic diseases are understood and managed.
Healthcare will shift from reactive treatment based on current symptoms to proactive, personalized interventions informed by an individual's long-term physiological trajectories.
- · Healthcare Providers
- · Patients with Chronic Diseases
- · AI in Healthcare Developers
- · Digital Health Platforms
- · Traditional diagnostic methods
- · One-size-fits-all treatment models
- · Insurance companies with static risk models
Machine learning models will become integral to chronic disease risk stratification and personalized treatment plans.
Longitudinal health data will be highly valued, leading to new data governance challenges and opportunities for health data platforms.
Predictive health insights could fundamentally reshape public health strategies, moving towards population-level preemptive interventions based on trajectory analysis.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG