Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts

Rinaldi, Alex; Tree, Jean E. Fox; Chaturvedi, Snigdha

doi:10.18653/v1/2020.acl-main.2

Cited by 12 publications

(7 citation statements)

References 43 publications

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“…Theme. Rinaldi et al [27] outlines an interesting approach to a binary classification problem: predicting whether the narrator is depressed or not based on latent categorization of interview prompts. The study proposes a new model called the Joint Latent Prompt Categorization (JLPC) model, which analyzes interview transcripts to predict depression while jointly categorizing interview prompts into latent categories.…”

Section: Pre-trained Language Models For Nlp Tasksmentioning

confidence: 99%

Towards Automatic Narrative Coherence Prediction

Bendevski

Ibrahim

Krulec

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Research in Psychology has shown that stories people tell about themselves, and how they recall their experiences, reveal a lot about their individual characteristics and mental well-being. The Narrative Coherence Coding Scheme (NaCCS) is a set of guidelines established in psychology research for annotating the "coherence" of a narrative along three dimensions: context, chronology and theme. A significant correlation was found between a narrative's coherence score and independently collected mental health markers of the narrator. Currently, all coherence annotations are done manually; a time consuming task which drains vital resources. In this paper, we propose an Artificial Intelligence based approach involving Natural Language Processing (NLP) to predict a narrative's coherence score (4-class classification problem). We explore a number of techniques, ranging from traditional machine learning models such as Support Vector Machines (SVM) to pre-trained language models such as BERT (Bidirectional Encoder Representations from Transformers). BERT produced the best results for all dimensions in terms of accuracy: 53.7% (context), 71.8% (chronology), and 69.6% (theme). The location of information in the narratives (beginning, end, throughout) was helpful in improving predictions.

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Section: Pre-trained Language Models For Nlp Tasksmentioning

confidence: 99%

Towards Automatic Narrative Coherence Prediction

Bendevski

Ibrahim

Krulec

et al. 2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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“…There has been an increase in recent years in the number of reported studies that have attempted to diagnose psychiatric disorders using natural language processing. Target disorders include schizophrenia [15,16], depression [17], bipolar affective disorder, obsessive-compulsive disorder [18], autism spectrum disorders [19], dementia [20,21] . CC-BY-NC-ND 4.0 International license It is made available under a perpetuity.…”

Section: Discussionmentioning

confidence: 99%

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

Kishimoto

Nakamura²,

Kano

et al. 2021

Preprint

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Introduction: Psychiatric disorders are diagnosed according to diagnostic criteria such as the DSM-5 and ICD-11. Basically, psychiatrists extract symptoms and make a diagnosis by conversing with patients. However, such processes often lack objectivity. In contrast, specific linguistic features can be observed in some psychiatric disorders, such as a loosening of associations in schizophrenia. The purposes of the present study are to quantify the language features of psychiatric disorders and neurocognitive disorders using natural language processing and to identify features that differentiate disorders from one another and from healthy subjects. Methods: This study will have a multi-center prospective design. Major depressive disorder, bipolar disorder, schizophrenia, anxiety disorder including obsessive compulsive disorder and, major and minor neurocognitive disorders, as well as healthy subjects will be recruited. A psychiatrist or psychologist will conduct 30-to-60-min interviews with each participant and these interviews will be recorded using a microphone headset. In addition, the severity of disorders will be assessed using clinical rating scales. Data will be collected from each participant at least twice during the study period and up to a maximum of five times. Discussion: The overall goal of this proposed study, the Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN), is to develop objective and easy-to-use biomarkers for diagnosing and assessing the severity of each psychiatric disorder using natural language processing. As of August 2021, we have collected a total of >900 datasets from >350 participants. To the best of our knowledge, this data sample is one of the largest in this field.

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“…Dialogue analysis gains attention in the field of mental health care. Rinaldi et al (2020) aim to predict depression based on the spoken data from interviews, and the multimodal approach that includes the audio features for depression detection in conversations. Other cognitive health issues can also be modeled in conversations (Farzana et al, 2020).…”

Section: Conversation-based Of Mental Disease Detectionmentioning

confidence: 99%

“…The researchers created the suicide knowledge graph to encode the utterance features as the input of Bi-LSTM layer, and then made a classification of the risk level with multilayer perceptron(MLP). In similar issue, Rinaldi et al (2020) aim to predict depression in the interviews. They make an evaluation on the Distress Analysis Interview Corpus (DAIC) (Gratch et al, 2014), which contains text transcripts and audio records of interviews for a clinical assessment for depression.…”

Section: Conversation-based Of Mental Disease Detectionmentioning

confidence: 99%

Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

Wang,

Huang,

Hsu

et al. 2023

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

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This paper investigates a crucial aspect of mental health by exploring the detection of suicidal ideation in spoken phone conversations between callers and counselors at a suicide prevention hotline. These conversations can be lengthy, noisy, and cover a broad range of topics, making it challenging for NLP models to accurately identify the caller's suicidal ideation. To address these difficulties, we introduce a novel, self-adaptive approach that identifies the most critical utterances that the NLP model can more easily distinguish. The experiments use real-world Lifeline transcriptions, expertly labeled, and show that our approach outperforms the baseline models in overall performance with an F-score of 66.01%. In detecting the most dangerous cases, our approach achieves a significantly higher F-score of 65.94% compared to the baseline models, an improvement of 8.9%. The selected utterances can also provide valuable insights for suicide prevention research. Furthermore, our approach demonstrates its versatility by showing its effectiveness in sentiment analysis, making it a valuable tool for NLP applications beyond the healthcare domain.

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Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts

Cited by 12 publications

References 43 publications

Towards Automatic Narrative Coherence Prediction

Towards Automatic Narrative Coherence Prediction

Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology

Self-Adapted Utterance Selection for Suicidal Ideation Detection in Lifeline Conversations

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