Boamente: A Natural Language Processing-Based Digital Phenotyping Tool for Smart Monitoring of Suicidal Ideation

Abstract

People at risk of suicide tend to be isolated and cannot share their thoughts. For this reason, suicidal ideation monitoring becomes a hard task. Therefore, people at risk of suicide need to be monitored in a manner capable of identifying if and when they have a suicidal ideation, enabling professionals to perform timely interventions. This study aimed to develop the Boamente tool, a solution that collects textual data from users' smartphones and identifies the existence of suicidal ideation. The solution has a virtual keyboard mobile application that passively collects user texts and sends them to a web platform to be processed. The platform classifies texts using natural language processing and a deep learning model to recognize suicidal ideation, and the results are presented to mental health professionals in dashboards. Text classification for sentiment analysis was implemented with different machine/deep learning algorithms. A validation study was conducted to identify the model with the best performance results. The BERTimbau Large model performed better, reaching a recall of 0.953 (accuracy: 0.955; precision: 0.961; F-score: 0.954; AUC: 0.954). The proposed tool demonstrated an ability to identify suicidal ideation from user texts, which enabled it to be experimented with in studies with professionals and their patients.

Keywords: artificial intelligence; deep learning; eHealth; mental health; mobile application; natural language processing; suicide.

Figure 1

Boamente system overview.

Figure 2

Methodology to find the best ML/DL model to be deployed in the inference engine.

Figure 3

Word clouds containing terms after text cleaning and stop words removal for (a) positive class and (b) negative class.

Figure 4

Number of instances labeled as negative and positive: (a) without data balancing (original dataset); (b) with 80% used for training and validation; (c) with 20% used for testing; and (d) with 80% used for training after applying SMOTE.

Figure 5

Cross-validation applied for evaluating the performance of all models.

Figure 6

Screenshots of the Boamente virtual keyboard.

Figure 7

Dashboards on the Boamente web application home screen.

Figure 8

Patient-specific dashboards on the Boamente web application.

Figure 9

Best results for accuracy and precision metrics.

Figure 10

Best results for recall and F-score metrics.

Figure 11

ROC curve with confidence interval of the BERTimbau Large model.

Figure 12

Comparison of our best model with the best one developed in Carvalho et al. [38].