PDF(Pubmed)
Abstract：
BACKGROUND: In an age when telehealth services are increasingly being used for forward triage, there is a need for accurate suicide risk detection. Vocal characteristics analyzed using artificial intelligence are now proving capable of detecting suicide risk with accuracies superior to traditional survey-based approaches, suggesting an efficient and economical approach to ensuring ongoing patient safety.
OBJECTIVE: This systematic review aimed to identify which vocal characteristics perform best at differentiating between patients with an elevated risk of suicide in comparison with other cohorts and identify the methodological specifications of the systems used to derive each feature and the accuracies of classification that result.
METHODS: A search of MEDLINE via Ovid, Scopus, Computers and Applied Science Complete, CADTH, Web of Science, ProQuest Dissertations and Theses A&I, Australian Policy Online, and Mednar was conducted between 1995 and 2020 and updated in 2021. The inclusion criteria were human participants with no language, age, or setting restrictions applied; randomized controlled studies, observational cohort studies, and theses; studies that used some measure of vocal quality; and individuals assessed as being at high risk of suicide compared with other individuals at lower risk using a validated measure of suicide risk. Risk of bias was assessed using the Risk of Bias in Non-randomized Studies tool. A random-effects model meta-analysis was used wherever mean measures of vocal quality were reported.
RESULTS: The search yielded 1074 unique citations, of which 30 (2.79%) were screened via full text. A total of 21 studies involving 1734 participants met all inclusion criteria. Most studies (15/21, 71%) sourced participants via either the Vanderbilt II database of recordings (8/21, 38%) or the Silverman and Silverman perceptual study recording database (7/21, 33%). Candidate vocal characteristics that performed best at differentiating between high risk of suicide and comparison cohorts included timing patterns of speech (median accuracy 95%), power spectral density sub-bands (median accuracy 90.3%), and mel-frequency cepstral coefficients (median accuracy 80%). A random-effects meta-analysis was used to compare 22 characteristics nested within 14% (3/21) of the studies, which demonstrated significant standardized mean differences for frequencies within the first and second formants (standardized mean difference ranged between -1.07 and -2.56) and jitter values (standardized mean difference=1.47). In 43% (9/21) of the studies, risk of bias was assessed as moderate, whereas in the remaining studies (12/21, 57%), the risk of bias was assessed as high.
CONCLUSIONS: Although several key methodological issues prevailed among the studies reviewed, there is promise in the use of vocal characteristics to detect elevations in suicide risk, particularly in novel settings such as telehealth or conversational agents.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD420200167413; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020167413.
OBJECTIVE: This systematic review aimed to identify which vocal characteristics perform best at differentiating between patients with an elevated risk of suicide in comparison with other cohorts and identify the methodological specifications of the systems used to derive each feature and the accuracies of classification that result.
METHODS: A search of MEDLINE via Ovid, Scopus, Computers and Applied Science Complete, CADTH, Web of Science, ProQuest Dissertations and Theses A&I, Australian Policy Online, and Mednar was conducted between 1995 and 2020 and updated in 2021. The inclusion criteria were human participants with no language, age, or setting restrictions applied; randomized controlled studies, observational cohort studies, and theses; studies that used some measure of vocal quality; and individuals assessed as being at high risk of suicide compared with other individuals at lower risk using a validated measure of suicide risk. Risk of bias was assessed using the Risk of Bias in Non-randomized Studies tool. A random-effects model meta-analysis was used wherever mean measures of vocal quality were reported.
RESULTS: The search yielded 1074 unique citations, of which 30 (2.79%) were screened via full text. A total of 21 studies involving 1734 participants met all inclusion criteria. Most studies (15/21, 71%) sourced participants via either the Vanderbilt II database of recordings (8/21, 38%) or the Silverman and Silverman perceptual study recording database (7/21, 33%). Candidate vocal characteristics that performed best at differentiating between high risk of suicide and comparison cohorts included timing patterns of speech (median accuracy 95%), power spectral density sub-bands (median accuracy 90.3%), and mel-frequency cepstral coefficients (median accuracy 80%). A random-effects meta-analysis was used to compare 22 characteristics nested within 14% (3/21) of the studies, which demonstrated significant standardized mean differences for frequencies within the first and second formants (standardized mean difference ranged between -1.07 and -2.56) and jitter values (standardized mean difference=1.47). In 43% (9/21) of the studies, risk of bias was assessed as moderate, whereas in the remaining studies (12/21, 57%), the risk of bias was assessed as high.
CONCLUSIONS: Although several key methodological issues prevailed among the studies reviewed, there is promise in the use of vocal characteristics to detect elevations in suicide risk, particularly in novel settings such as telehealth or conversational agents.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD420200167413; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020167413.
摘要：
背景：在远程医疗服务越来越多地用于前诊的时代，需要准确的自杀风险检测。使用人工智能分析的声乐特征现在证明能够检测自杀风险，其准确性优于传统的基于调查的方法。建议一种有效和经济的方法来确保持续的患者安全。
目的：本系统评价旨在确定哪些声音特征在区分自杀风险较高的患者与其他队列相比表现最好，并确定用于得出每个特征的系统的方法学规范和结果分类的准确性。
方法：通过Ovid搜索MEDLINE，Scopus,计算机和应用科学完成，CADTH,WebofScience,ProQuest论文和论文A&I,澳大利亚在线政策，Mednar于1995年至2020年进行，并于2021年进行了更新。入选标准是没有语言的人类参与者，年龄,或设置限制；随机对照研究，观察性队列研究，和论文；使用某种声音质量衡量标准的研究；使用经过验证的自杀风险衡量标准，与其他风险较低的个体相比，个体被评估为自杀风险较高。使用非随机研究工具中的偏倚风险评估偏倚风险。在报告声音质量的平均测量值的任何地方，都使用随机效应模型荟萃分析。
结果：搜索产生了1074个独特的引文，其中30例(2.79%)通过全文筛选。共有21项研究涉及1734名参与者，符合所有纳入标准。大多数研究（15/21，71％）通过VanderbiltII数据库（8/21，38％）或Silverman和Silverman感知研究记录数据库（7/21，33％）获取参与者。在区分高自杀风险和比较队列方面表现最佳的候选声音特征包括语音时间模式（中位数准确率为95％），功率谱密度子带（中值精度90.3%），和梅尔频率倒谱系数(中值准确度80%)。随机效应荟萃分析用于比较14％（3/21）的研究中嵌套的22个特征，这证明了第一和第二共振峰内频率的显着标准化平均差（标准化平均差在-1.07和-2.56之间）和抖动值（标准化平均差=1.47）。在43%(9/21)的研究中，偏倚风险评估为中度，而在其余研究中(12/21，57%)，偏倚风险被评估为高.
结论：尽管在所审查的研究中普遍存在几个关键的方法学问题，使用声音特征来检测自杀风险的升高是有希望的，特别是在新颖的环境中，如远程医疗或会话代理。
背景：PROSPERO国际系统评价前瞻性注册CRD420200167413;https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42020167413。
目的：本系统评价旨在确定哪些声音特征在区分自杀风险较高的患者与其他队列相比表现最好，并确定用于得出每个特征的系统的方法学规范和结果分类的准确性。
方法：通过Ovid搜索MEDLINE，Scopus,计算机和应用科学完成，CADTH,WebofScience,ProQuest论文和论文A&I,澳大利亚在线政策，Mednar于1995年至2020年进行，并于2021年进行了更新。入选标准是没有语言的人类参与者，年龄,或设置限制；随机对照研究，观察性队列研究，和论文；使用某种声音质量衡量标准的研究；使用经过验证的自杀风险衡量标准，与其他风险较低的个体相比，个体被评估为自杀风险较高。使用非随机研究工具中的偏倚风险评估偏倚风险。在报告声音质量的平均测量值的任何地方，都使用随机效应模型荟萃分析。
结果：搜索产生了1074个独特的引文，其中30例(2.79%)通过全文筛选。共有21项研究涉及1734名参与者，符合所有纳入标准。大多数研究（15/21，71％）通过VanderbiltII数据库（8/21，38％）或Silverman和Silverman感知研究记录数据库（7/21，33％）获取参与者。在区分高自杀风险和比较队列方面表现最佳的候选声音特征包括语音时间模式（中位数准确率为95％），功率谱密度子带（中值精度90.3%），和梅尔频率倒谱系数(中值准确度80%)。随机效应荟萃分析用于比较14％（3/21）的研究中嵌套的22个特征，这证明了第一和第二共振峰内频率的显着标准化平均差（标准化平均差在-1.07和-2.56之间）和抖动值（标准化平均差=1.47）。在43%(9/21)的研究中，偏倚风险评估为中度，而在其余研究中(12/21，57%)，偏倚风险被评估为高.
结论：尽管在所审查的研究中普遍存在几个关键的方法学问题，使用声音特征来检测自杀风险的升高是有希望的，特别是在新颖的环境中，如远程医疗或会话代理。
背景：PROSPERO国际系统评价前瞻性注册CRD420200167413;https://www.crd.约克。AC.uk/prospro/display_record.php?ID=CRD42020167413。
