School of Languages and Cultures, University of Sydney, Sydney 2006, Australia.
Department of Computer Science, City University of Hong Kong, Hong Kong 518057, China.
Int J Environ Res Public Health. 2021 Sep 24;18(19):10048. doi: 10.3390/ijerph181910048.
Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people.
We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases.
We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM).
Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher ( < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR-) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR- (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic.
Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.
在线心理健康信息是有心理健康问题的人重要的资源。尽管人们对更具操作性的心理健康资源的需求不断增加，尤其是在年轻人中，但是心理健康信息是否适合有效自我护理的研究仍然不足。
我们旨在开发贝叶斯机器学习分类器，作为评估有心理健康问题和疾病的人获取可信心理健康信息的可操作性的基于数据的决策辅助工具。
我们收集并将可信的在线心理健康信息分为一般心理健康 (GEN) 信息和特定患者 (PAS) 心理健康信息，并对其进行分类。GEN 和 PAS 都是由心理健康和促进公共卫生的卫生当局开发的以患者为导向的健康资源。GEN 是没有针对性阅读群体指示的未分类在线健康信息；PAS 是有针对性地为特定人群（年轻人、老年人、孕妇和男性）开发的，这是由他们的网站标签表明的。为了确保模型的通用性，我们选择使用相关性向量机 (RVM) 开发稀疏贝叶斯机器学习分类器。
使用优化和归一化技术，我们通过自然语言特征的联合优化和特征频率的 min-max 归一化，开发出了性能最佳的分类器。最佳模型的 AUC（0.957）、灵敏度（0.900）和特异性（0.953）在统计学上均高于使用结构和语义特征的并行优化的其他模型（<0.05），无论是否进行特征归一化。随后，我们通过比较阳性似然比 (LR+) 和阴性似然比 (LR-) 以及 95%置信区间 (95%CI)，随着我们将概率阈值调整为 0.1 和 0.9 的范围，在临床上评估了我们模型的诊断效用。我们发现，当概率阈值设置为 0.45 时，LR+（18.031，95%CI：10.992，29.577）和 LR-（0.100，95%CI：0.068，0.148）的最佳配对具有最高的诊断性能，其灵敏度为 0.905（95%：0.867，0.942），特异性为 0.950（95%CI：0.925，0.975）。我们模型的这些统计特性表明其在临床上的适用性。
我们的研究发现，与一般心理健康信息相比，特定患者心理健康信息在信息可操作性、参与度和对具有不同心理健康问题的特定人群的适用性方面具有显著优势。GEN 更适合一般心理健康信息的获取，而 PAS 可以有效地吸引患者并提供更有效和必要的自我护理支持。开发的贝叶斯机器学习分类器提供了自动工具，以支持临床决策，以识别更具操作性的资源，从而有效支持不同人群的自我护理。
