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.

1. Use ACE Scores in Machine Learning to Predict
Disease Earlier and Improve Outcomes
Michael Mastanduno, PhD
Yannick Van Huele, PhD

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Machine Learning in Healthcare
Machine learning in healthcare uses data,
an algorithm, and a model to predict an
event and suggest interventions that can
improve the outcome of that event.
A machine learning model for a health
system could be designed to predict,
for example, who in the hospital is
likely to get a central line-associated
bloodstream infection (CLABSI).
Clinicians could then pay special
attention to infection control best
practices for those identified patients.

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Machine Learning in Healthcare
Initially, the machine is fed historical data
(e.g., the health system’s patient attributes
for those that did and did not get a CLABSI
over the past two years), which an algorithm
uses to learn relationships (e.g., historical
CLABSI rates relative to patient age and
comorbidities, duration of catheter insertion,
and catheter type used).

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Machine Learning in Healthcare
This is the essence of the machine learning
workflow, which stores the relationships and
applies them to make the prediction (e.g., given
this patient’s similarities to all the historical
CLABSI cases, he has a 75 percent chance of
getting an infection today).
The model is trained with new data as it
becomes available (e.g., CLABSI cases over
the next six months), which improves the
reliability of future predictions.

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What is Machine Learning?
The components and processes of a machine learning model to predict a healthcare outcome.

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Machine Learning in Healthcare
Health systems can use machine learning to
predict sepsis, the likelihood of readmission
or missing an appointment, and dozens of
other clinical and operational conditions.
From the workflow described on the previous
slide, it’s evident that “nutrient-rich” data
sources are necessary to feed predictive
algorithms in a machine learning model
that’s designed to improve health outcomes.

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Machine Learning in Healthcare
An important health system objective is making
accurate predictions, which relies on capturing a
data snapshot of the entire patient.
Adverse Childhood Experience (ACE) scores fill
one of the significant gaps health systems
typically don’t have data for.

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The Data Headwaters of ACE Scores
From 1995 to 1997, the CDC and Kaiser
Permanente conducted a landmark survey of
more than 17,000 people to learn about their
early childhood experiences (e.g., abuse,
relationships, drug and alcohol use, etc.) and
current health statuses.
Participants were asked about ten types of
childhood trauma related to abuse, household
challenges, and neglect, and were assigned an
ACE score on a scale from 0 to 10.

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The Data Headwaters of ACE Scores
The participants were also asked about
personal and family health.
The study showed a strong correlation between
high ACEs and negative health outcomes later
in life, including risky health behaviors, chronic
health conditions, reduced lifetime income, and
early death.
While national efforts aim to prevent child abuse
altogether, much can be done later in life to
prevent further consequences from those early
experiences.

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The Data Headwaters of ACE Scores
The opportunities for collecting ACE data are
relatively rare, either during childhood or a
primary care visit later in life if the doctor is
involved with a data-collecting program.
But ACE scores only need to be collected once;
they never change, and, as the CDC puts it,
provide tremendous insight into a “person’s
future violence victimization and perpetration,
and lifelong health and opportunity.”

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Using ACE Data to Improve Individual and
Community Health
ACE data is typically used in public health programs for
state- and community-wide prevention efforts.
In the context of machine learning, health
systems should use it to benefit individual
patients, so they can flag them as high
risk, treat them appropriately, and
hopefully prevent ACE-related
conditions from surfacing.
Family Health History and Health
Appraisal questionnaires are readily
available instruments for establishing
ACE scores.

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Using ACE Data to Improve Individual and
Community Health
These are new sources of information to
consider and systems should broadly
adopt surveys as instruments for
improving population and individual health.
The better picture organizations can paint
of a person’s health history, the better they
can predict the need for future
interventions.
The better the incoming data, the better
the predictions.

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Using ACE Data to Improve Individual and
Community Health
Here’s an example of how health systems can
use ACE scores and machine learning to improve
patient outcomes:
A patient named Alex is admitted to the ED at a
hospital where a machine learning model is
used to predict opioid addiction risk.
The model discovers the strong relation between
high ACE scores and opioid abuse in the historical
data, and flags Alex as being high risk for addiction.
Clinicians act on this information by avoiding
an opioid prescription but also treat the
underlying factors that make Alex more
prone to abusing opioids.

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Health Systems Need Feature Engineering on
All Source Data
It’s possible to have bad data or too much data,
so feature engineering separates the wheat from
the chaff. Feature engineering encodes data into
formats that are useful for machine learning.
For example, if a health system thinks a patient’s
addiction risk may have a seasonal component,
then it must convert the date column from
“August 14, 2014” to “August.”
Once the system gets the data, it can select how
it will feed it into the model, and whether to toss,
keep, separate, or combine certain variables.
This applies to all source data, including
ACE scores.

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Improve Outcomes with ACE Data
As health systems rely more heavily on machine
learning, they must keep in mind that a machine
learning model is only as good as the data it
receives.
Machine learning in healthcare depends on high-
quality data to improve outcomes; ACE scores
help build better data sets.
Incorporating “nutrient-rich” data sources, such
as information about ACEs into machine learning
models, can improve their ability to predict
negative health outcomes, therefore allowing for
earlier interventions.

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Improve Outcomes with ACE Data
Clinical data collection is moving in
the right direction.
Today, organizations have data that
can be used for machine learning on
current problems; in the future, ACE
scores, eating habits, and lifestyle
data will all be combined to predict
diseases earlier and significantly
improve population health
management.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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More about this topic
Link to original article for a more in-depth discussion.
Use ACE Scores in Machine Learning to Predict Disease Earlier and Improve Outcomes
How Machine Learning in Healthcare Saves Lives
Levi Thatcher, Director of Data Science
How Healthcare Text Analytics and Machine Learning Work Together to Improve Patient
Outcomes – Mike Dow, Technical Director; Levi Thatcher, Director of Data Science
How Healthcare AI Makes Machine Learning Accessible to Everyone in Healthcare
Levi Thatcher, Director of Data Science
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future of Predictive
Analytics in Healthcare – Dale Sanders, Senior VP, Strategy
Three Approaches to Predictive Analytics in Healthcare
David Crockett, Ph.D, Senior Director of Research and Predictive Analytics

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Mike Mastanduno joined Health Catalyst in November of 2016 as a Data Scientist. He
received his PhD from Dartmouth College in Biomedical Engineering, where he
designed hardware and software tools to aid in the early diagnosis of breast cancer.
Mike’s dissertation culminated in a 60-patient clinical trial to evaluate the technology he
had developed. Mike then went on to a postdoctoral fellowship at the Stanford School of
Medicine where he won a National Institute of Health award to study medical imaging of ovarian
cancers. Since joining Health Catalyst, Mike has been focused on outcomes improvements through
machine learning. Mike has had a hand in development of a high-performance heart failure
readmissions risk model, a service line predictor that saves greater than 1 million dollars per year,
and a sophisticated statistical model to find high-cost imaging utilization. Mike’s current focus is on
over-utilization and image processing.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Michael Mastanduno, PhD

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
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.
Yannick Van Huele, PhD