ananana / mental-disorders

This repository contains code for training and using deep learning models for mental disorder detection from social media data.

Install

The required libraries are:

• tensorflow2, keras
• numpy, nltk, sklearn, pandas

The full list of packages and versions I used is found in requirements.txt (may contain some unnecessary ones)

The required data:

• expects a config.json in the root directory (containing paths to resource files)
• for generating predictions, expects files with trained model weights and their hyperparameter configurations in models/
• expects external resources (NRC lexicon, LIWC lexicon, pre-trained embeddings) in resources/
• expects vocabulary files and a LIWC cache file in data/

Usage

predict_erisk.py illustrates how models can be loaded and used to generate predictions on eRisk data.

predict(run_nr, data_rounds) can be used to obtain predictions (scores and alerts ("decision"s) for each user ("nick"), for each datapoint) from a specific trained model given some data obtained from the eRisk server, across one or more rounds of interaction with the server.

For more control, EriskDataGenerator() (along with model.predict_step()) can be used directly, which allows to incrementally add new datapoints to the generator as they are received from the server (without creating a new loader for each new datapoint).

Model

Publications

The code in this repository has been used for experiments published in several papers. If using this resource, please cite the relevant papers:

• Explainability of Depression Detection on Social Media: From Deep Learning Models to Psychological Interpretations and Multimodality, Ana-Sabina Uban, Berta Chulvi, Paolo Rosso, in Early Detection of Mental Health Disorders by Social Media Monitoring, 2022
• Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios, Petr Lorenc, Ana-Sabina Uban, Paolo Rosso, Jan Šedivý, NLDB 2022
• Multi-aspect transfer learning for detecting low resource mental disorders on social media, Ana Sabina Uban, Berta Chulvi, Paolo Rosso, LREC 2022
• On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data, Ana Sabina Uban, Berta Chulvi, Paolo Rosso, NLDB 2021
• An emotion and cognitive based analysis of mental health disorders from social media data, Ana-Sabina Uban, Berta Chulvi, Paolo Rosso, Future Generation Computer Systems, 2021
• Upv-symanto at erisk 2021: Mental health author profiling for early risk prediction on the internet, Angelo Basile, Mara Chinea-Rios, Ana-Sabina Uban, Thomas Müller, Luise Rössler, Seren Yenikent, María Alberta Chulvi-Ferriols, Paolo Rosso, Marc Franco-Salvador, CLEF 2021
• Deep learning architectures and strategies for early detection of self-harm and depression level prediction, Ana-Sabina Uban, Paolo Rosso, CLEF 2020