STUDENTLIFEPREDICTIVE MODELINGHongyu Chen

Jing Li

Mubing Li

CS69/169 Mobile Health

March 2015

Motivation• Let’s go further than StudentLife 1.0!

• Standardized, normalized data set• Proof of concept• Scientific finding to our question:

Can we predict depression from a two week window

of StudentLife data collection?

• Study Design• Data cleaning/parsing• Feature selection• Class determination• Predictive classifiers through supervised machine learning

methods• Validation• Case study

StudentLife Dataset

PHQ9 Threshold

Non-depressed whole time

Depressed whole time

Depresstion status changed

EMA: SleepMoodStressSocialExercise, etc.

Sensor:AudioConversationActivityDark,etc.

Feature

Class

SVM, etc.

Prediction

N-fold CV

Result analysis

Accruacy F statisticsPrecision/ RecallSensitivity/Specificity

Data Preprocessing & Interpolation:• Linear • Nearest

Neighbour • Concatenation

Case Study

PCA

Data separation by week

Project Design/Workflow

Feature Selection

Class Determination via Thresholding• Why?

• Keeps it a classification problem, not a regression problem• Depression presents in many different ways• Small sample size

Class Determination via Thresholding

PHQ-9 scores of students before and after StudentLife

Class Determination via Thresholding• Threshold determined by visual inspection on strip plot

Class Determination via Thresholding• Consistent with medical literature?

PHQ-9 score Diagnosis

0-4 No Depression

5-9 Mild Depression

10-14 Moderate Depression

15-19 Moderately Severe Depression

20-27 Severe Depression

Do you at least moderate depression?

Linear Interpolation for EMA Data

EMA data is very sparse

Interpolation increases number of points

Nearest-Neighbor Interpolation for Sensor Data

Sensor data is too dense

Interpolation decreases number of points

Standardized Data Set?• In the first iteration of StudentLife:

• Every data collection modality had• Different scaling• Different periodicity• Different quality

• Now:• All 15 depression-related modalities have

• One value per 24-hour period• Comparable scaling• A guarantee of good quality (279 samples removed)

Feature Selection• Step 1:

• Decide sliding window time frame• Two weeks

• Balance of enough time to make diagnosis, but short enough to have enough time points for testing

• Step 2:• Feature aggregation

• Step 3:• Dimensionality Reduction• We cannot use 105 dimensions to classify only a couple hundred

cases!

Principle Component Analysis (PCA)

Top Features from PCA

Random Forest

Random Forest

Decision Trees

Predictive classifier Classes: (not depressed, depressed)

-1 +1

Features: top features by PCA

Training set

All depressedSamples(50%)

Selected not depressedSamples(50%)

SVM model Cross Validation Accuracy = 96.6667%

Case study

● Participant No.16:Beginning of the term: Not depressed (-1)

End of the term: Depressed (+1)

Not depressed

Depressed

Future Directions• Why is this important?

1. Contributes (marginally) to existing medical literature about depression

2. Proof of concept for possible interventionsImagine app that tells you when you could be depressed

Connects you with resources to help

3. Standardized data set availableOpens door to future analyses

Not only on depression

Small taste of the beginnings of…

StudentLife2.0?