data_heterogeneity

Repository for paper "Machine learning for passive mental health symptom prediction: generalization across different longitudinal mobile sensing studies"

Last updated: 04/28/2022

Paper citation: Daniel A. Adler, Fei Wang, David C. Mohr, and Tanzeem Choudhury. 2022. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 17, 4: e0266516.

Questions?

If you have any questions email me: dadler@infosci.cornell.edu

Raw Data

You can download the StudentLife and CrossCheck public datasets from the following sources:

Raw CrossCheck data: link. Download the "CrossCheck_Daily_Data.csv" file.

Raw StudentLife data: link. Download the entire data zip file.

Folder Structure

data

Cleaned data, created by the notebooks/data_cleaning.ipynb notebook, is provided in the "data" folder. The cleaned data has behavior features-EMAs aligned between the datasets, by taking the average across the 3-days up to and including the day the EMA was taken. See the "Sensor-EMA Alignment Across Studies" subsection in the paper. The files are:

• CrossCheck data: crosscheck_daily_data_cleaned_w_sameday.csv
• StudentLife data: studentlife_daily_data_cleaned_w_sameday_08282021.csv

notebooks

• data_cleaning.ipynb: Processes raw CrossCheck/StudentLife data to produce the behavior feature-EMA aligned files. You will need to change paths throughout the notebook after downlading the raw CrossCheck/StudentLife data to use this file.
• Demographics_and_Distributions.ipynb: Creates all figures/statistics in the paper to document demographic information from the cleaned data.
• simple_regression_models_analysis.ipynb: Processes model results and produces relevant tables/figures. Runs statistical tests.

code

• cleaning_util.py: Utilities for data cleaning. You will need to change certain paths throughout the notebook
• util.py: Various utility functions used throughout the code
• run_cv.py: Runs the regression_cv.py LOSO-CV code
• regression_cv.py: LOSO-CV code

res

This folder is left blank, but you will see that various results files are used in the simple_regression_models_analysis.ipynb notebook. I was not able to push the output files to github because the filesize is too large.

You can produce the LOSO-CV results file, called "loso_res_df_all_v10_10182021.csv.gz" by running the following command:

python run_cv.py

And in the run_cv.py file setting these variables to the following:

params_dict = {
    'gbt': {
        'loss': ['huber'],
        'learning_rate': [0.001, 0.01, 0.1, 1.0],
        'n_estimators': [20, 100, 1000],
        'max_depth': [3, 7, 10],
        'random_state': [42]
    }
}
output_filename ='../res/loso_res_df_all_v10_10182021.csv.gz'

These are the hyperparameters used for the model results in the paper.