Hi, there. My name is Hsien-Te. I am a researcher at USC Information Sciences Institute. I am
excited to talk about user-based collaborative filtering mobile health system. Let’s start
with why we need this mobile health system. It is a frustrating wait to see the doctor.
This is a persistent problem in the healthcare industry around the world. Mobile health systems
offer a solution to the healthcare crisis. Multimodal sensors enable personalized health
prediction with contexts. Major tech corporations and federal agencies have invested in mobile
health systems, building the next-generation healthcare infrastructure. There are three
key challenges in mobile health: usability, predictability, and interpretability. Users
are reluctant to provide their health information over privacy concerns. It is challenging to
make predictions without sufficient historical data. The health community requires interpretable
AI medical decisions. We conducted a ten-week longitudinal study TILES to assess the health
conditions of 212 hospital workers using mobile devices, wearables, and sensors. We used a
subset of TILES dataset to train and test our system. For psychometrics, we selected
job performance, health, and affect. For sensors, we selected one audio sensor (Jelly Mobile
Phone), two biosensors (OMSignal Shirt/Bra and Fitbit Charge 2), and two environmental
sensors (Owl-in-One and Minew). We aggregated the sensor data by day to align with daily
health labels. We proposed a user-based collaborative filtering mobile health system that leverages
the health information from other participants as user history to enable cold-start prediction
and personalized modeling. There are three parts: cohort formation, cohort assignment,
and cold-start prediction. Part 1 – Cohort Formation. User similarity is measured by
health labels, variable relationships, and sensor data. For health labels, the system
measures distribution differences by descriptive statistics. For variable relationships, our
system measures relationship differences by cross-model prediction. For sensor data, the
system measures signal differences by feature importance. Afterward, the system clusters
the users into cohorts. Users can differ in their health labels, sensor data, and variable
relationships. Previous studies often focus on a single clustering target that overlooks
user similarity in the other two dimensions. Our method maximizes user similarity within
each cluster across all three dimensions. This is a cohort profile for positive affect.
There are four cohorts. These cohorts have different health label ranges, top sensor
signals, and variable relationships. The cohort profile shows interpretable differences across
health labels, sensor data, and variable relationships. Part 2 – Cohort Assignment. The new user
would provide five health labels and sensor data. The cohort classification models would
decide if the new user belonged to that cohort. The regression model would serve as a tiebreaker.
Our system measure cohort similarity by model prediction. We look at how many health labels
(one, three, five, and seven) our system needs to perform cohort assignments for 12 constructs. Our system
has promising cohort assignments with five health labels for most of the constructs.
Part 3 – Cold-Start Prediction. Now, our system has assigned the new user to a cohort
and discard the five health labels. The system would leverage cohort information as user
history to perform personalized predictions. The system outperforms the baseline model
that does not leverage cohort information and personalization. Again, our system beats
the individual model trained with five health labels. The individual model trained with
all health labels shows how much the system can improve. Our system has strong health
predictions without historical data. In conclusion, our mobile health system addresses three key
challenges: usability, predictability, and interpretability. Users only need to provide
five health labels for cohort assignments. The system discards the labels afterward.
Health predictions show promising performance without historical data. Cohort profiles are
interpretable to the users and healthcare community. Our system is a step closer to
future AI doctors. Thank you for listening.