Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Graduation Program in Psychiatry and Department of Psychiatry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
PLoS One. 2018 Oct 24;13(10):e0204820. doi: 10.1371/journal.pone.0204820. eCollection 2018.
The present study analyzes the feasibility of text classification to predict individual suicidal behavior. Entries from Virginia Woolf's diaries and letters were used to assess whether a text classification algorithm could identify written patterns associated with suicide.
This is a text classification study. We compared 46 text entries from the two months before Virginia Woolf's suicide with 54 texts randomly selected from Virginia Woolf's work during other periods of her life. Letters and diaries were included, while books, novels, short stories, and article fragments were excluded. The data was analyzed using a Naïve-Bayes machine-learning algorithm.
The model showed a balanced accuracy of 80.45%, sensitivity of 69%, and specificity of 91%. The Kappa statistic was 0.6, which means a good agreement, and the p-value of the model was 0.003. The area under the ROC curve (AUC) was 0.80. In other words, the model exhibited good performance when used for classifying Virginia Woolf's diaries and letters.
The present study showed the feasibility of a machine-learning model coupled with text to identify individual written patterns associated with suicidal behavior. Our text signature was able to identify the period of two months preceding suicide with a high accuracy. This technique may be applied to subjects with psychiatric disorders by means of data captured from social media, e-mail, among others. The algorithm may then predict a specific outcome and enable early intervention by clinicians.
本研究分析了文本分类预测个体自杀行为的可行性。使用弗吉尼亚·伍尔夫的日记和信件来评估文本分类算法是否能够识别与自杀相关的书面模式。
这是一项文本分类研究。我们将弗吉尼亚·伍尔夫自杀前两个月的 46 个文本条目与她生命中其他时期随机选择的 54 个文本进行了比较。包括信件和日记，但排除了书籍、小说、短篇小说和文章片段。使用朴素贝叶斯机器学习算法对数据进行分析。
该模型的平衡准确率为 80.45%，敏感度为 69%，特异性为 91%。Kappa 统计量为 0.6，表明有较好的一致性，模型的 p 值为 0.003。ROC 曲线下面积（AUC）为 0.80。换句话说，该模型在对弗吉尼亚·伍尔夫的日记和信件进行分类时表现出良好的性能。
本研究表明，结合文本的机器学习模型在识别与自杀行为相关的个体书面模式方面具有可行性。我们的文本特征能够以较高的准确率识别自杀前两个月的时期。该技术可以通过社交媒体、电子邮件等方式从精神病患者那里获取数据，应用于有精神障碍的患者。然后，该算法可以预测特定的结果，并使临床医生能够进行早期干预。
