WHITE PAPER
China’s Meridian Medical Networks
Uses Trusted Analytics Platform
(TAP) to Build Big Data-Driven
Hypertension Risk Model
China faces sharply rising
healthcare costs due to a
number of factors, including:
• An aging population
• Skyrocketing growth of
chronic disease
• A severe shortage of
qualified medical
practitioners
• A strained primary care
system
• Overcrowded hospitals for
its 1.3 billion people
Executive Summary
As per capita medical costs grow at 14 to 18 percent1, outpacing expansion of gross
domestic product (GDP), China must reshape its healthcare infrastructure and
delivery approaches. The Chinese government is addressing these needs through
a five-year plan (2016 to 2020), which promotes a proactive care model. The plan
calls for improvements to a range of services including routine health screening,
preventive care, chronic-disease management, rehabilitation and infectious
disease control.
Health checkup centers (HCCs), which began as a healthcare segment a decade ago
in China, represent one key trend to help fill the gaps. The main business for an
HCC (which can be a department of an existing hospital or an independent entity)
is to provide routine health examinations that are paid for by employers as part
of employee benefits. China already has 10,000 HCCs, and the number grows at
an annual rate of 27 percent. Up until now, HCCs have focused on expanding their
user base and cutting costs to grow revenues. But that’s changing, as leading HCCs
begin increasing the quality and personalization of services for health checkups
and follow-up health management.
Meridian Medical Network Corp. (Meridian) is an innovative healthcare software
and solution developer that serves the HCC market by building a smart health
system, including backend big data technologies and frontend mobile applications
and monitoring systems. Meridian’s core technology consists of an integrated
architecture that links big data analytics and clinical pathways within a healthcare
Internet of Things (IoT) system.
Meridian’s big data analysis strategy for healthcare incorporates data from
both the hospital and consumer. Hospital-generated data include health
checkup data, clinical medical records and survey information, and serve as the
general “baseline” for providing healthcare services for consumers with certain
health conditions. Consumer-generated data include vital signs data streamed
continuously from healthcare IoT devices, plus dynamic questionnaire answers
from consumers gathered through applications provided by Meridian, which
together serve as the personal “calibration” for determining healthcare services
for a specific consumer. Meridian’s big data analysis takes into consideration both
general “baseline” and personal “calibration” data to provide a hierarchy of health
China’s Meridian Medical Networks Uses Trusted Analytics Platform (TAP) to Build Big Data-Driven Hypertension Risk Model
Solution Highlights
• TAP helped Meridian scale
beyond the capacity of
existing analytics tools,
with the ability to perform
analysis on 20 million
individual checkup records,
with nearly 700 indices and
annotations
• TAP streamlined cleanup
and preparation of data
from two years of checkup
results from nearly 310,000
patients
• The project successfully
integrated clinical data
with self-reported status
information (calorie intake,
exercise, etc.) plus selfmeasured real-time healthcare data from healthcare
monitors (blood pressure
and blood sugar meters,
digital weight scales, fitness
trackers, etc.)
• Meridian tested three
different risk models using TAP’s Analytics Tool Kit
(ATK) to determine which
model performed best in
predicting hypertension
• Meridian’s scalable model
built on TAP provides an
important infrastructure for
future patient engagement
efforts by HCC clients
information that enables the best
assessment and intervention plan for
consumers.
With a focus on improving patient
health, Meridian recently collaborated
with Intel Corporation to build a big
data-driven model to identify patients
at highest risk of hypertension, an
asymptomatic condition that affects
330 million people in China. Meridian
and Intel chose the open source
Trusted Analytics Platform (TAP) to
build their analytic models and service
applications because of its ability to
handle large data sets and streamline
the analytics workflow.
Business Drivers
Hypertension increases the odds of
heart attack, stroke and kidney failure.
Unless mechanisms for early detection
are put into place at scale, hypertension
will continue to grow as a public health
and economic concern. Meridian turned
to TAP to help handle the volume and
complexity of its data for predicting
which patients are at highest risk of
having or developing this condition that
would otherwise progress undetected.
Figure 1. The Anatomy of TAP
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“By employing TAP’s big data analytics
platform, Meridian is able to integrate
massive data sets from a wide variety
of sources, and then call a rich set of
standard machine-learning algorithms
to analyze the data and develop models
that capture insights into predictors of
hypertension,” said Dr. John Yu, founder
and CEO at Meridian Medical Network
Corp. “TAP’s platform greatly simplifies
workflows and reduces development
time when creating applications
powered by big data analytics, helping
us to bring more targeted assessments
and interventions to the public faster,
and with less cost.”
Meridian collects healthcare data
for 20 million customers from
various partners, including physical
examination results with nearly 700
indexes and annotations. Its business
model is B2B2C (Business-to-Businessto-Consumer), through which Meridian
partners with healthcare service
providers to provide their customers
with new services. To engage more
consumers more directly with their
own healthcare, and deliver an
appropriate personal intervention
plan to the right person at the right
China’s Meridian Medical Networks Uses Trusted Analytics Platform (TAP) to Build Big Data-Driven Hypertension Risk Model
Trusted Analytics Platform™
TAP is open source software optimized for performance and
security that accelerates the creation of cloud-native applications
driven by big data analytics. TAP makes it easier for application
developers and data scientists — at enterprises, cloud service
providers, and system integrators — to collaborate by providing
a shared, flexible environment for advanced analytics in public
and private clouds. Data scientists get extensible tools, scalable
machine learning algorithms and powerful engines to train and
deploy predictive models. Application developers get consistent
APIs, services and runtimes to help integrate these models into
applications quickly. System operators get an integrated stack
that they can easily provision in a cloud infrastructure.
TAP helps lower development costs and reduces time to deploy
analytics applications for organizations that want to create custom solutions using big data analytics. Tested by data scientists
in various industries, TAP uniquely provides a complete pipeline
for graph analysis as well as scalable algorithms and in-memory
engines for machine learning. TAP delivers open source software
as an integrated platform with hardware-enhanced performance
and security in every layer.
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time, Meridian wants to harness
the full power of big data analytics
to build up Meridian’s innovative,
proprietary Health Assessment Incident
Graph (HAIG). The HAIG will include
all popular anomaly indices or risk
factors that are interconnected in a
directed, weighted graph to specify the
correlation between the risk factors.
When applying the HAIG to a specific
customer and incorporating the
customer-generated healthcare data,
it will produce a dynamically evolving
“blueprint” representing the customer’s
overall health conditions over time,
from which a personalized health
assessment and related intervention
plan can be made.
As the data volume continues to rise,
with more and more HCCs joining the
partnership, Meridian will be able to
analyze the data to improve health
assessment models through big data
analytics. The company also intends
to quickly develop and deploy various
health management applications based
on these models.
The Process
The model-generating process includes
two major parts: Data Preparation and
Model Development.
Data Preparation
To produce meaningful insights that
provide a more personal and valuable
pre- and post-checkup service,
Meridian needed to aggregate and
process the data before analysis.
Although it’s possible to process
data manually, it is extremely timeconsuming and difficult to pull all data
together in a common format, especially
when the data volume is large. Different
HCCs may use proprietary IT systems
and different medical equipment and
methods. So test results collected
on the same checkup item may use
a different measurement unit with a
different reference range. For example,
the unit for TG (triglyceride) could be
mmol/L or mg/dL.
Figure 2. Meridian Healthcare IoT System Architecture
Meridian pooled longitudinal data of
nearly 310,000 patients, which included
China’s Meridian Medical Networks Uses Trusted Analytics Platform (TAP) to Build Big Data-Driven Hypertension Risk Model
two years of checkup results. The raw
data came from different checkup
centers, ingested into TAP in the CSV
format. Cleaning and qualifying this
amount of data to achieve consistency
is typically a great deal of work.
However, TAP includes a number of
built-in tools for data cleaning and
transformation, such as:
• String normalization or splitting
a specific string into several
variables.
• Removing or inputting specific
values to missing records.
• Identifying abnormal data or
errors, removing them or inputting
specific values to replace them.
• Transforming a continuous variable
to a classified variable.
These data preparation tools are
integrated into TAP, enabling the data
preparation and cleaning process –
which usually involves the collaboration
of a data scientist, a clinician, and a
technical engineer – to be completed
much more quickly, easily and
efficiently (with typical time savings
from weeks to days).
Three models have been trained for the
hypertension risk prediction project:
Random Forest (RF), Logistic Regression
(LR), and Support Vector Machine
(SVM). For each model, subjects were
divided into two cohorts: the derivation
cohort (a randomly chosen 80 percent
subset of the study population) and
the validation cohort (the remaining
20 percent of the study population).
Meridian will be able to run multiple
models to determine which is most
accurate, based on confirmation with
clinical follow-up tests.
Those patients who registered for
the post-checkup hypertension
management service will be given
monitoring devices such as a blood
pressure meter, a fitness band, a digital
scale, etc. The dynamic data collected
from the monitoring devices will be
analyzed and a list of variables (for
example, the actual daily amount of
exercise vs. the planned daily amount
Model Development
After the data was qualified, Meridian
used TAP to begin analysis modeling.
Meridian’s data scientists and clinicians
planned to develop and test various
data models, each designed to predict a
person’s risk of becoming hypertensive
within three years. Traditional data
analysis tools, such as SAS or IBM
SPSS, presented several barriers to
such modeling, including:
• Inability to process checkup data
from more than 100,000 patients,
due to memory and computational
limitations.
• Inability to quickly deploy
predictive models into direct-toconsumer applications.
• Expensive upgrades to overcome
the above limitations.
Figure 3. Model Development With TAP
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of exercise, etc.) will be derived to
reflect the effectiveness of the health
intervention plan to fine-tune the
models. TAP can develop further
predictive modeling algorithms
to foresee how different personal
behaviors influence health risk change.
As a result, an intelligent dynamic
assignment of health interventions can
be created for a more targeted health
management experience.
Applying the Hypertension Risk
Model to the Mobile Health
Application
Meridian data scientists and clinicians
are still improving the trained models
by incorporating more data and testing
different prediction algorithms. At the
same time, the development team will
apply the generated hypertension risk
model to update the mobile health
management application called “Smart
Health Assistant.”
China’s Meridian Medical Networks Uses Trusted Analytics Platform (TAP) to Build Big Data-Driven Hypertension Risk Model
The original Smart Health Assistant
application was designed to allow
consumers to keep track of daily health
status data such as food and liquids
intake, exercise, blood sugar level,
blood pressure, and more, and to
provide some suggestions for better
health management. To improve the
user engagement level, Meridian
intends to add new user experience
designs based on hypertension risk
models, with graphic assessment
presentations that quantitatively
demonstrate the risk level of generating
hypertension. The Smart Health
Assistant application will also identify
contributing risk factors, and show the
dynamic evolution of the hypertension
risk level over time as the intervention
of risk factors progresses.
Once the hypertension risk model has
been fully trained and evaluated, it can
easily be deployed into the Meridian’s
production network via TAP’s scoring
engine, which is automatically installed
as part of the TAP ATK repositories. As
the Smart Health Assistant application
takes action managing consumers’
health, consumer-generated data is
continuously captured and fed back
into the TAP platform to further
calibrate the hypertension risk model
for each individual consumer, to
eventually develop a dynamically
1
evolving “personal model” for every
consumer—a new facilitator for
personalized medicine.
In the future, Meridian is planning to
explore other disease risk prediction
models — such as diabetes,
cardiovascular disease, and cancer—
based on the TAP platform so that
its health management services can
expand to a wider scope of consumers
with an overall optimization of access,
cost and quality.
Summary
To address rising medical costs and
other issues in China, healthcare
systems are shifting from treatmentand prescription-centric models to a
proactive patient-centric model that
is based on adoption and promotion
of healthy behaviors and health
management. Big data analytics
technologies make this new paradigm
possible by helping enterprises better
understand patient needs and provide
tools to empower people to more
actively participate in their own health.
By employing smart health solutions,
such as those in development by
Meridian, Chinese HCCs can extend
services both before and after regular
health checkups. Big data analytics
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can help HCCs provide more targeted
healthcare; inform payers, such as
employers, about the value of their
healthcare programs; and encourage
patients to engage in preventive
behaviors to benefit their personal
health outcomes.
Using TAP, data scientists and clinicians
are able to quickly analyze historical
medical data to provide insights into
personal managed care delivery,
building a risk prediction model
that HCCs can use to identify those
individuals at risk of developing specific
health conditions, and target those
who are most likely to benefit from
personalized interventions.
TAP provides data preparation tools,
an analytics modeling environment,
algorithm library, and an analytics
pipeline, all integrated into a data
science platform that greatly simplifies
workflows and reduces development
time when creating applications
powered by big data analytics.
To learn more about the Trusted
Analytics Platform, visit:
www.trustedanalytics.org.
Data from The Innovation Center for Social Risk Governance in Health, Fudan University, http://srghealth.fudan.edu.cn
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