Contextual-CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (SAMHSA) shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the US. Prior research involving surveys and questionnaires (e.g. PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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“…We have implemented a convolutional neural network (CNN) as proposed in [30] for our contextual classification task [53].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Gaur¹,

Alambo²,

Sain³

et al. 2019

The World Wide Web Conference

120

158

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“…This method is commonly used in visual imagery. With the advancement of machine learning over the years, the application scope of the CNN has become much broader, coping competently with an array of complex tasks ranging from image recognition (Cireşan et al 2012) to video analysis and human action recognition (Ji et al 2013) and text classification (Shin et al 2018).…”

Section: What Is Machine Learning?mentioning

confidence: 99%

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Luo

Cruz

Tzioutzios

2020

Int J Disaster Risk Sci

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Natural hazard-triggered technological accidents (Natechs) refer to accidents involving releases of hazardous materials (hazmat) triggered by natural hazards. Huge economic losses, as well as human health and environmental problems are caused by Natechs. In this regard, learning from previous Natechs is critical for risk management. However, due to data scarcity and high uncertainty concerning such hazards, it becomes a serious challenge for risk managers to detect Natechs from large databases, such as the National Response Center (NRC) database. As the largest database of hazmat release incidents, the NRC database receives hazmat release reports from citizens in the United States. However, callers often have incomplete details about the incidents they are reporting. This results in many records having incomplete information. Consequently, it is quite difficult to identify and extract Natechs accurately and efficiently. In this study, we introduce machine learning theory into the Natech retrieving research, and a Semi-Intelligent Natech Identification Framework (SINIF) is proposed in order to solve the problem. We tested the suitability of two supervised machine learning algorithms, namely the Long Short-Term Memory (LSTM) and the Convolutional Neural Network (CNN), and selected the former for the development of the SINIF. According to the results, the SINIF is efficient (a total number of 826,078 records were analyzed) and accurate (the accuracy is over 0.90), while 32,841 Natech reports between 1990 and 2017 were extracted from the NRC database. Furthermore, the majority of those Natech reports (97.85%) were related to meteorological phenomena, with hurricanes (24.41%), heavy rains (19.27%), and storms (18.29%) as the main causes of these reported Natechs. Overall, this study suggests that risk managers can benefit immensely from SINIF in analyzing Natech data from large databases efficiently.

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“…Table 3 shows an aggregation of discrete posts of same user in TvarM (Table 4) to predict the C-SSRS suicide risk levels. Our implementation of CNN is well described in Gaur et al [13] and Kim et al [80] and is suitable for our contextual classification task [81]. The model takes embeddings of user posts as input and classifies them into one of the suicide risk severity levels.…”

Section: Plos Onementioning

confidence: 99%

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

et al. 2021

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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.

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Contextual-CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification

Cited by 22 publications

References 7 publications

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS

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