Hello everyone, my name is Zhang Yunke from Tsinghua University. It is my pleasure to
share our work Passive Health Monitoring Using Large Scale Mobility Data that I did with
Doctor Xu Fengli, Doctor Li Tong, Professor Vassilis Kostakos, Professor Hui Pan, and
Professor Li Yong.
I’ll introduce our work in 4 parts. Before we go into the detail of our methods, I’ll
first introduce the background and research problems.
People’s interest in health monitoring has consistently grown in past years, currently
more than half of mobile phone users track their personal health status with health applications.
However, traditional health monitoring methods like questionnaire and custom-designed hardware
are difficult to implemented large-scaled due to intensive human labor and high expense.
Alternatively, mobility data offers a novel opportunity for passive health status monitoring.
It has the advantage of passively collecting, as well as providing a proxy to measure the
level of physical activities and interactions with various locations, which has been proven
informative to infer health status. These advantages inspire us to investigate the feasibility
of using mobility data to monitor health status.
Specifically, we want to explore the correlations between mobility data and users’ health
status, and, design a predictive model for large-scale health monitoring. Challenges
on the experiment are the collection of appropriate mobility data source for health status monitoring
and unclear mechanisms of mobility’s influence on health status.
To address the challenges, we propose three key designs in this research. First, we collect
a large scale mobility dataset with high spatio-temporal resolution. Then, we propose various health-related
features extracted from mobility trace records. Third, we predict health status with these
features and analyze how they influence health prediction.
Collecting an appropriate data source for health status monitoring is essential, here
we collect the mobility trace record data of 2112 users as well as their health status
and demographic information. Half of the users are outpatient of Beijing’s major hospitals.
Others report good health status. They authorized us to collect up to 2 months of mobility traces
records that capture their visits across the metropolitan area of Beijing. More information
on demographic features are shown in the table. To demonstrate user’s interaction with urban
space, we adopt Beijing’s Point of Interest dataset. This dataset contains more than a
million POIs with geographic location and categorical information.
After we obtain users’ mobility trace and health status, we need to extract well-defined
and meaningful features from mobility traces to understand the relationship between mobility
and health. We design following features to characterize health-related patterns: The
radius of gyration, rho, depicts user’s vigor of mobility. The standard deviation
of displacements, 𝜎, characterizes the regularity of mobility. The distribution entropy
of locations visited. 𝜖, captures the diversity of movements. The above features are regarded
as “mobility features”. Then, we characterize user’s neighborhood environment features
by counting the health-related POIs and green space area around their home. We consider
POIs in food, entertainment, scenic spots, and sports categories and fastfood store and
tobacco-liquor shop sub-categories. Finally, the places where users visit can also reveal
their health status. We calculate the POI environment around each visited location and
take the average as user's visitation environment features.
To study the feasibility of using mobility features to predict hospital visits, we train
a binary XGBoost classifier to predict user’s health status with demographic and mobility
trace features. We report the AUC and F1-score of using different feature combinations as
the classifier’s input. To interpret feature importance in the prediction
model, we adopt the analysis framework of SHAP values. It measures feature’s additive
importance for a particular prediction. Features with positive SHAP value in a prediction leads
to a higher probability of predicting the user to be unhealthy.
From the results of prediction performance, we can first recognize that prediction with
all features as input has the best performance, and it is significantly higher than using
only demographic features as input.
In addition, comparing the first and fifth row reveals that the combination of mobility
trace features outperforms the prediction made with demographic features. These findings
provide strong evidence that it is possible to use mobility trace data to monitor health
status.
Here we show the results of feature importance analysis. Red bar in the right figure means
that higher feature value correlates with higher possibility of predicting unhealthy.
Blue bar is correlated with healthy.
First, age, the standard deviation of displacements, income, and entertainment category POI density
around visited points are most important features.
Then, from the color bars, we can interpret that users are more likely to be predicted
as healthy if they have a higher coverage area of movements, more diversity in visit
pattern, more scenic sights, sports facilities, less tobacco store and entertainment venues
in neighborhood, more visits to sports and scenic sights and less visits to entertainment
venues.
To conclude, we investigate the correlations between large scale passively collected mobility
dataset and overall health status. We extract features from mobility traces that can significantly
enhance the performance of health status prediction. We provide deeper insights into how mobility
features affect the health status prediction by feature importance analysis. These findings
offer implications on real-time large-scale overall health monitoring.
Thank you all for your attention and I am glad to take any questions.