JMIR formative research · 2026
A Smartphone-Based Acoustic Machine Learning Pipeline for Detecting Suicidal Ideation: Case-Control Model Development and Validation Study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Lyu Min, Tan Lixin, Xiao Jun, Huang Heqing, Liu Fangjian, Qi Jiahui, Huang Tian, Lei Jinyu, Zhao Zhihui, Jiang Tianxiang, Liu Zhu, Wang Xueqian, Zhong Jiang, Feng Zhengzhi
Аннотация
BACKGROUND: Suicidal ideation (SI) among university students is a growing public health concern. Self-report screening can be limited by concealment and delayed disclosure. We evaluated a leakage-resistant, proof-of-concept pipeline to detect SI from standardized smartphone-recorded speech.
OBJECTIVE: This study aimed to extract acoustic markers from brief smartphone-based reading tasks and develop machine learning models for suicide risk prediction in university students, enabling low-cost, scalable early screening to support campus mental health services.
METHODS: Questionnaire data and speech recordings were collected via a WeChat mini program. After screening and clinical confirmation, 96 participants (n=48, 50% with SI; n=48, 50% controls) were included. Age and sex were evaluated as potential confounders. Each participant read 16 standardized sentences. Acoustic features were extracted using openSMILE (version 3.0.2), yielding a 570D feature vector per utterance. To prevent leakage from multiple recordings per speaker, we used participant-level 5-fold cross-validation, assigning all recordings from each participant to a single fold. Seven machine learning algorithms were evaluated using area under the curve (AUC), accuracy, and F1-score.
RESULTS: Acoustic-based models discriminated participants with SI from control participants. The SI group was significantly older than the control group (P=.001). Random forest achieved an AUC of 0.813 (accuracy=0.748), and naive Bayes achieved an AUC of 0.806 (accuracy=0.757). Feature families related to pitch, mel-frequency cepstral coefficients, and harmonicity contributed to model performance.
CONCLUSIONS: Standardized read speech captured via smartphones shows preliminary feasibility for SI discrimination under a leakage-aware evaluation design. External validation and testing with more naturalistic speech are warranted.
OBJECTIVE: This study aimed to extract acoustic markers from brief smartphone-based reading tasks and develop machine learning models for suicide risk prediction in university students, enabling low-cost, scalable early screening to support campus mental health services.
METHODS: Questionnaire data and speech recordings were collected via a WeChat mini program. After screening and clinical confirmation, 96 participants (n=48, 50% with SI; n=48, 50% controls) were included. Age and sex were evaluated as potential confounders. Each participant read 16 standardized sentences. Acoustic features were extracted using openSMILE (version 3.0.2), yielding a 570D feature vector per utterance. To prevent leakage from multiple recordings per speaker, we used participant-level 5-fold cross-validation, assigning all recordings from each participant to a single fold. Seven machine learning algorithms were evaluated using area under the curve (AUC), accuracy, and F1-score.
RESULTS: Acoustic-based models discriminated participants with SI from control participants. The SI group was significantly older than the control group (P=.001). Random forest achieved an AUC of 0.813 (accuracy=0.748), and naive Bayes achieved an AUC of 0.806 (accuracy=0.757). Feature families related to pitch, mel-frequency cepstral coefficients, and harmonicity contributed to model performance.
CONCLUSIONS: Standardized read speech captured via smartphones shows preliminary feasibility for SI discrimination under a leakage-aware evaluation design. External validation and testing with more naturalistic speech are warranted.