回顾机器学习算法通过语音模式检测风险行为的有效性。

IF 2.4 Q4 NEUROSCIENCES
Haripriya Nagasubramanian, Saranya T S
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引用次数: 0

摘要

背景:任何冒险行为都可能导致消极的结果。这在很大程度上取决于情绪的复杂相互作用和个人对风险的感知。人工智能和机器学习可以研究个体的生物特征和语言,这可以帮助临床医生进行个性化的结构化干预。摘要:本综述探讨了人工智能和基于机器学习的算法如何用于检测风险行为,以及它们的诊断特征和治疗结果。该综述收集了所有关于使用现有ML技术进行风险检测的现代研究,以及它们对临床实践的积极影响。本研究探讨了如何应用各种深度学习模型来提高诊断结果的准确性和可靠性。关键信息:尽管许多ML模型在检测风险行为方面显示出强大的潜力,但它们确实面临局限性,如精度和灵敏度的次优水平、有限的临床价值、外部有效性、高假阳性率和较低的可解释性。因此,HMM被推荐为一个很好的替代方案,因为它在从公开行为中发现隐藏状态方面表现出色,特别是使用语言或语音分析。目前在风险预测领域的研究主要是对文本或语音进行分析,并使用神经成像数据。DL实践的实施需要通过验证,同时也要考虑伦理方面的考虑,数据隐私问题也要考虑。有强有力的证据表明,深度学习和机器学习模型及其适应性显示出预测和预防危险行为的有希望的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reviewing the Effectiveness of Machine Learning Algorithm for Detecting Risk Behaviours Through Speech Patterns.

Background: Any risk behaviour may result in a negative outcome. This highly depends on the complex interplay of emotions and an individual's perception of risk. AI and ML can study the biological signature and speech of individuals, which can help clinicians intervene with individualised structured interventions.

Summary: This review investigates how AI and ML-based algorithms are used for detecting risk behaviours such as along with their diagnostic characteristics and treatment results. The review serves to collect all modern research about risk detection using existing ML techniques, along with their positive impact on clinical practice. The research explores how applying various DL models enhances the diagnostic accuracy and reliability of the findings.

Key message: Though many ML models show a strong potential in detecting the risk behaviours, they do face limitations like a sub-optimal level of precision and sensitivity, Limited clinical value, external validity, high false positive rates, and less interpretability. Hence, HMM is recommended as a good alternative because of its excellence in uncovering the hidden states from overt behaviours, especially using language or speech analysis. The research currently in the field of risk prediction works on text or speech analysis and uses neuroimaging data. The implementation of DL practice is needed through validation, and at the same time, ethical considerations, data privacy issues should also be considered. There is strong evidence to suggest that DL and ML models and their adaptations show promising ways to predict and prevent risky behaviours.

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来源期刊
Annals of Neurosciences
Annals of Neurosciences NEUROSCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
39
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