Dataset Information

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

ABSTRACT:

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.

SUBMITTER: de Avila Berni G

PROVIDER: S-EPMC6200194 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

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

de Ávila Berni Gabriela G Rabelo-da-Ponte Francisco Diego FD Librenza-Garcia Diego D Boeira Manuela V MV Kauer-Sant'Anna Márcia M Passos Ives Cavalcante IC Kapczinski Flávio F

PloS one 20181024 10

<h4>Background</h4>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.<h4>Methods</h4>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 per ...[more]

PMID: 30356303

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Project description:IntroductionDifficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.MethodsBiometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).ResultsThese models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037).DiscussionDeveloping interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

Dataset Information

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

Background

Methods

Results

Discussion

Publications

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

Similar Datasets

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