Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

• URL: http://arxiv.org/abs/2104.04140v1
• Date: Fri, 9 Apr 2021 01:39:41 GMT
• Title: Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS
• Authors: Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth
• Abstract summary: We develop deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors. The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.
• Score: 9.424631103856596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential - most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.

Related papers

• An Exploratory Deep Learning Approach for Predicting Subsequent Suicidal Acts in Chinese Psychological Support Hotlines [13.59130559079134]
  The accuracy of scale-based predictive methods for suicide risk assessment can vary widely depending on the expertise of the operator. This study is the first to apply deep learning to long-term speech data to predict suicide risk in China.
  arXiv  Detail & Related papers  (2024-08-29T11:51:41Z)
• SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
  Sepsis is the leading cause of in-hospital mortality in the USA. Existing predictive models are usually trained on high-quality data with few missing information. For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
  arXiv  Detail & Related papers  (2024-07-24T04:47:36Z)
• SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis [22.709733830774788]
  This study presents a Chinese social media dataset designed for fine-grained suicide risk classification. Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10. Deep learning models show good performance in distinguishing between high and low suicide risk, with the best model achieving an F1 score of 88.39%.
  arXiv  Detail & Related papers  (2024-04-19T06:58:51Z)
• Non-Invasive Suicide Risk Prediction Through Speech Analysis [74.8396086718266]
  We present a non-invasive, speech-based approach for automatic suicide risk assessment. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. Our most effective speech model achieves a balanced accuracy of $66.2,%$.
  arXiv  Detail & Related papers  (2024-04-18T12:33:57Z)
• Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data [2.76101452577748]
  The COVID-19 pandemic has escalated mental health crises worldwide. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression. As these conditions develop, signs of suicidal ideation may manifest in social media interactions.
  arXiv  Detail & Related papers  (2023-12-13T17:15:12Z)
• CenTime: Event-Conditional Modelling of Censoring in Survival Analysis [49.44664144472712]
  We introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance.
  arXiv  Detail & Related papers  (2023-09-07T17:07:33Z)
• Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes [51.378225388679425]
  We present an end-to-end multitask system to address a novel task of detection of Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance.
  arXiv  Detail & Related papers  (2022-05-20T06:31:08Z)
• An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms [0.0]
  This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect suicidal intentions. The proposed model demonstrated an accuracy of 90.3 percent and an F1-score of 92.6 percent.
  arXiv  Detail & Related papers  (2021-12-17T15:34:03Z)
• Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses [15.732431764583323]
  We use medical claims data to study the risk of subsequent suicide attempts for patients who were hospitalized due to suicide attempts and later discharged. Almost 20% of "suspected" suicide attempts are identified from diagnosis codes indicating external causes of injury and poisoning with undermined intent.
  arXiv  Detail & Related papers  (2020-09-05T20:47:16Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks [43.2802002858859]
  This paper enhances text representation with lexicon-based sentiment scores and latent topics. It proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators.
  arXiv  Detail & Related papers  (2020-04-16T11:18:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.