Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes

• URL: http://arxiv.org/abs/2206.06141v1
• Date: Fri, 20 May 2022 06:31:08 GMT
• Title: Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes
• Authors: Soumitra Ghosh, Asif Ekbal and Pushpak Bhattacharyya
• Abstract summary: 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.
• Score: 51.378225388679425
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, 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, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB.

Related papers

• 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)
• 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)
• Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes [21.374488755816092]
  The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances.
  arXiv  Detail & Related papers  (2024-03-28T14:03:12Z)
• Depression Detection on Social Media with Large Language Models [23.075317886505193]
  Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media. We propose a novel depression detection system called DORIS, combining medical knowledge and the recent advances in large language models.
  arXiv  Detail & Related papers  (2024-03-16T01:01:16Z)
• Ontology-aware Learning and Evaluation for Audio Tagging [56.59107110017436]
  Mean average precision (mAP) metric treats different kinds of sound as independent classes without considering their relations. Ontology-aware mean average precision (OmAP) addresses the weaknesses of mAP by utilizing the AudioSet ontology information during the evaluation. We conduct human evaluations and demonstrate that OmAP is more consistent with human perception than mAP.
  arXiv  Detail & Related papers  (2022-11-22T11:35:14Z)
• Multi-modal Depression Estimation based on Sub-attentional Fusion [29.74171323437029]
  Failure to diagnose depression leads to over 280 million people suffering from this psychological disorder worldwide. We tackle the task of automatically identifying depression from multi-modal data. We introduce a sub-attention mechanism for linking heterogeneous information.
  arXiv  Detail & Related papers  (2022-07-13T13:19:32Z)
• Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting [47.93070579578704]
  We explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
  arXiv  Detail & Related papers  (2022-05-06T17:47:05Z)
• Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS [9.424631103856596]
  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.
  arXiv  Detail & Related papers  (2021-04-09T01:39:41Z)
• Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach [55.41644538483948]
  This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units. The aim is to support decision making addressed at reducing the incidence rate of infections.
  arXiv  Detail & Related papers  (2020-05-07T16:13:12Z)

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.