Ntalampiras Stavros, Qi Wen
Department of Computer Science, University of Milan, 20135 via Celoria 18, Milan, Italy.
School of Future Technology, South China University of Technology, 510641 Wushan Road 381, Guangzhou, China.
J Healthc Inform Res. 2024 Oct 3;8(4):577-593. doi: 10.1007/s41666-024-00175-4. eCollection 2024 Dec.
The evaluation of an individual's mental health and behavioral functioning, known as psychological assessment, is generally conducted by a mental health professional. This process aids in diagnosing mental health conditions, identifying suitable treatment options, and assessing progress during treatment. Currently, national health systems are unable to cope with the constantly growing demand for such services. To address and expedite the diagnosis process, this study suggests an AI-powered tool capable of delivering understandable predictions through the automated processing of the captured speech signals. To this end, we employed a Siamese neural network (SNN) elaborating on standardized speech representations free of domain expert knowledge. Such an SNN-based framework is able to address multiple downstream tasks using the same latent representation. Interestingly, it has been applied both for classifying speech depression as well as assessing its severity. After extensive experiments on a publicly available dataset following a standardized protocol, it is shown to significantly outperform the state of the art with respect to both tasks. Last but not least, the present solution offers interpretable predictions, while being able to meaningfully interact with the medical experts.
对个人心理健康和行为功能的评估，即心理评估，通常由心理健康专业人员进行。这一过程有助于诊断心理健康状况、确定合适的治疗方案以及评估治疗过程中的进展。目前，国家卫生系统无法应对对此类服务不断增长的需求。为了应对并加快诊断过程，本研究提出了一种人工智能驱动的工具，该工具能够通过对捕获的语音信号进行自动处理来提供可理解的预测。为此，我们采用了一种暹罗神经网络（SNN），它基于标准化的语音表示，无需领域专家知识。这种基于SNN的框架能够使用相同的潜在表示来处理多个下游任务。有趣的是，它已被应用于对语音抑郁进行分类以及评估其严重程度。在遵循标准化协议对一个公开可用的数据集进行广泛实验后，结果表明，在这两项任务上，该方法均显著优于现有技术。最后但同样重要的是，本解决方案提供可解释的预测，同时能够与医学专家进行有意义的互动。
