JMIR Publications

ABSTRACT

Background:

Crisis hotlines serve as a crucial avenue for early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in crisis hotline context is constrained by factors such as lack of non-verbal communication cues, anonymity, time limitations, and single-occasion intervention. Therefore, it is necessary to develop approaches including acoustic features for identifying suicide risk among hotline callers early and quickly. Given the complicated features of the sound, adopting artificial intelligence models to analyze callers’ acoustic features is promising.

Objective:

In this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models.

Methods:

We collected audio data from crisis hotline calls, and extracted various acoustic features, including basic acoustic features and high-level statistical function features (HSFs). The performance of prediction models based on basic acoustic features was compared with those utilizing HSFs.

Results:

The development of machine learning models utilizing HSF acoustic features has been demonstrated to enhance recognition performance compared to models based solely on basic acoustic features. The random forest classifier, developed with HSFs, achieved the best performance in detecting suicide risk among the models evaluated(Accuracy = 0.75, F1-score = 0.70, Recall = 0.76, False Negative Rate = 0.24).

Conclusions:

These results demonstrated the potential of developing AI-based early warning systems using acoustic features in identifying suicide risk among crisis hotline callers. Our work also has implications for employing acoustic features to identify suicide risk in voice salient contexts. Clinical Trial: The present study has been approved by the Institutional Review Board of Tianjin University (2024-453). The researchers confirm that all stages of this study were conducted in accordance with the ethical standards set forth by the Helsinki Declaration, as revised in 1989. Prior to being connected with a hotline operator, callers were informed via an automated message that their calls would be recorded and that any data obtained from these calls would be treated in accordance with the tenets of confidentiality and analyzed in an anonymized manner.