I-HOPE — Interpretable Hierarchical mOdel for Personalized mEntal Health Prediction

This repository contains code for I-HOPE — Interpretable Hierarchical mOdel for Personalized mEntal Health Prediction. I-HOPE is a mental health prediction system that employs a two-stage hierarchical model to map raw behavioral features to mental health status (PHQ-4 categories). It does so by leveraging five defined behavioral categories, referred to as interaction labels. This work utilizes the CES dataset(https://www.kaggle.com/datasets/subigyanepal/college-experience-dataset).

Overview

The project follows a two-stage hierarchical model as shown below:

1. Stage 1: Feature Mapping to Interaction Labels
   35 chosen raw behavioral features are transformed into five interaction labels:
   • Leisure
   • Me Time
   • Phone Time
   • Sleep
   • Social Time
     This is achieved by data cleaning, feature engineering, clustering (using KMeans), and personalized feature importance analysis (using Random Forests).

Selected Features and Relevant Labels

#	Feature Name	Relevant Labels[0: Leisure, 1: MeTime, 2: Phone, 3: Sleep , 4: SocialInt]
1	act_on_bike_ep_0	[0,1]
2	act_on_foot_ep_0	[0,1,4]
3	act_running_ep_0	[0,1]
4	act_still_ep_0	[1,3]
5	act_walking_ep_0	[0,1,4]
6	audio_convo_duration_ep_0	[0,2,4]
7	(call_in_num + call_out_num) / (call_in_duration + call_out_duration)	[0,2]
8	loc_food_audio_voice	[4]
9	loc_home_audio_voice	[1,2,3]
10	loc_social_audio_voice	[0,2,4]
11	loc_other_dorm_audio_voice	[0,4]
12	loc_self_dorm_audio_voice	[1,2]
13	loc_study_audio_voice	[1,4]
14	loc_food_convo_duration	[4]
15	loc_home_convo_duration	[1,2,3]
16	loc_other_dorm_convo_duration	[0,4]
17	loc_social_convo_duration	[0,4]
18	loc_study_convo_duration	[1,4]
19	loc_self_dorm_convo_duration	[1,2]
20	loc_home_dur	[1,3]
21	loc_leisure_dur	[0,4]
22	loc_other_dorm_dur	[4]
23	loc_self_dorm_dur	[1,3]
24	loc_social_dur	[4]
25	loc_study_dur	[1,4]
26	loc_workout_dur	[0,1]
27	loc_home_unlock_num / loc_home_unlock_duration	[0,1,2]
28	loc_other_dorm_unlock_num / loc_other_dorm_unlock_duration	[0,4,2]
29	loc_self_dorm_unlock_num / loc_self_dorm_unlock_duration	[1,2]
30	loc_social_unlock_num / loc_social_unlock_duration	[2,4]
31	loc_study_unlock_num / loc_study_unlock_duration	[1,2,3]
32	sleep_duration	[3]
33	sleep_end - sleep_start	[3]
34	unlock_num_ep_0 / unlock_duration_ep_0	[2]
35	sleep_heathkit_dur	[3]

2. Stage 2: Prediction Using Interaction Labels
   The computed interaction label scores are then used as inputs to build personalized neural network models to predict the PHQ-4 mental health categories.

Files

1. Github_code.ipynb -- Notebook with the code
2. Csvs used -
   • Initial dataset: Got after running Section 2(a) of https://github.com/bill-wei-xuan/Unlocking-MentalHealth/blob/main/notebooks/DigitalWellbeing.ipynb
   • Manual labels : CHASE_Labeled.csv
   • All the original data can be downloaded from https://www.kaggle.com/datasets/subigyanepal/college-experience-dataset