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Michael Mastanduno, PhD

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Michael Mastanduno, PhD

Machine Learning 101: 5 Easy Steps for Using it in Healthcare

Machine learning, used in the context of healthcare, is not about computers replacing doctors or rolling robots dispensing bedside care to patients. Perhaps a better term would be data-driven healthcare because it is the process of using historical patient data in a predictive model to determine the likelihood of a healthcare-related outcome. The black box that is the machine is no more than an algorithm trained by data. Most importantly, the predictions can be used by doctors to optimize decision making in real time, thus reducing readmissions, infection rates, and other complications that drive up costs and lower the quality of care.
This article explains some machine learning basics, dispels some misconceptions, and outlines five steps to its implementation:

Define the use case.
Prepare the data.
Train the model.
Make predictions on new data.
Deliver the risk score for use in clinical decision support.

Machine learning is destined to be a digital partner for physicians, executives, and health systems focused on improving clinical, financial, and operational performance. To get there, it must first be understood.

Michael Mastanduno, PhD
Yannick Van Huele, PhD

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.