Virginia Woolf, neuroprogression, and bipolar disorder

Family history and traumatic experiences are factors linked to bipolar disorder. It is known that the lifetime risk of bipolar disorder in relatives of a bipolar proband are 5-10% for first degree relatives and 40-70% for monozygotic co-twins. It is also known that patients with early childhood trauma present earlier onset of bipolar disorder, increased number of manic episodes, and more suicide attempts. We have recently reported that childhood trauma partly mediates the effect of family history on bipolar disorder diagnosis. In light of these findings from the scientific literature, we reviewed the work of British writer Virginia Woolf, who allegedly suffered from bipolar disorder. Her disorder was strongly related to her family background. Moreover, Virginia Woolf was sexually molested by her half siblings for nine years. Her bipolar disorder symptoms presented a pernicious course, associated with hospitalizations, suicidal behavioral, and functional impairment. The concept of neuroprogression has been used to explain the clinical deterioration that takes places in a subgroup of bipolar disorder patients. The examination of Virgina Woolf’s biography and art can provide clinicians with important insights about the course of bipolar disorder

Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings.

BACKGROUND: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. METHODS: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. RESULTS: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. DISCUSSION: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

Correction: Potential use of text classification tools as signatures of suicidal behavior: A proof-of-concept study using Virginia Woolf's personal writings.

[This corrects the article DOI: 10.1371/journal.pone.0204820.]