Learn more about Yannick Van Huele, PhD

Author Bio

Yannick Van Huele joined Health Catalyst in May 2017 as a Data Science Intern. Prior to coming to Health Catalyst, he was a graduate student at the University of Washington where he studied algebraic number theory and received a PhD in Mathematics.

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Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes

The Adverse Childhood Experience (ACE) study conducted by the CDC and Kaiser Permanent showed a strong correlation between ACEs and negative health outcomes later in life (e.g., risky health behaviors, chronic health conditions, and early death). ACE scores help paint a more complete picture of a person’s health history—a more comprehensive data snapshot of the entire patient.
Given that ACE scores build better data sets and machine learning relies on high-quality data, health systems should incorporate these nutrient-rich data sources into their machine learning models to better predict negative health outcomes, allow for earlier interventions, and improve outcomes.
Healthcare machine learning is evolving to use ACE scores and lifestyle data (e.g., eating habits) to improve population health management.

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