A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department

Pestian, John; Grupp‐Phelan, Jacqueline; Cohen, K. Bretonnel; Meyers, Gabriel; Richey, Linda; Matykiewicz, Paweł; Sorter, Michael T.

doi:10.1111/sltb.12180

Cited by 63 publications

(97 citation statements)

References 21 publications

(24 reference statements)

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“…Efforts to understand suicide risks can be roughly clustered into traits or states. Trait analyses focus on stable characteristics rooted in and measured using biological processes (Costanza et al., ; Le‐Niculescu et al., ), whereas state analyses measure dynamic characteristics like verbal and nonverbal communication, termed “thought markers” (Pestian et al., ). Machine learning and natural language processing have successfully identified differences in retrospective suicide notes, newsgroups, and social media (Gomez, ; Huang, Goh, & Liew, ; Matykiewicz, Duch, & Pestian, ).…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Pestian

Sorter

Connolly

et al. 2016

Suicide & Life Threat Behav

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Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Pestian

Sorter

Connolly

et al. 2016

Suicide & Life Threat Behav

Self Cite

119

118

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“…The MINI provides results for both lifetime and current presence of disorders. Subjects were asked five open‐ended Ubiquitous Questions (UQs) to harvest the language that would serve as the input to the machine‐learning model: Do you have hope? Do you have any secrets?…”

Section: Methodsmentioning

confidence: 99%

“…A Support Vector Machine (SVM) model was used for classification . SVMs have been proven useful for classification problems such as these, due to their robustness to overfitting and ability to perform well in high‐dimensional spaces . Linear and radial kernels were all considered in training the classifiers, and the kernel with the best overall performance was chosen.…”

Section: Methodsmentioning

confidence: 99%

Identifying epilepsy psychiatric comorbidities with machine learning

et al. 2020

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Objective People with epilepsy are at increased risk for mental health comorbidities. Machine‐learning methods based on spoken language can detect suicidality in adults. This study's purpose was to use spoken words to create machine‐learning classifiers that identify current or lifetime history of comorbid psychiatric conditions in teenagers and young adults with epilepsy. Materials and Methods Eligible participants were >12 years old with epilepsy. All participants were interviewed using the Mini International Neuropsychiatric Interview (MINI) or the MINI Kid Tracking and asked five open‐ended conversational questions. N‐grams and Linguistic Inquiry and Word Count (LIWC) word categories were used to construct machine learning classification models from language harvested from interviews. Data were analyzed for four individual MINI identified disorders and for three mutually exclusive groups: participants with no psychiatric disorders, participants with non‐suicidal psychiatric disorders, and participants with any degree of suicidality. Performance was measured using areas under the receiver operating characteristic curve (AROCs). Results Classifiers were constructed from 227 interviews with 122 participants (7.5 ± 3.1 minutes and 454 ± 299 words). AROCs for models differentiating the non‐overlapping groups and individual disorders ranged 57%‐78% (many with P < .02). Discussion and Conclusion Machine‐learning classifiers of spoken language can reliably identify current or lifetime history of suicidality and depression in people with epilepsy. Data suggest identification of anxiety and bipolar disorders may be achieved with larger data sets. Machine‐learning analysis of spoken language can be promising as a useful screening alternative when traditional approaches are unwieldy (eg, telephone calls, primary care offices, school health clinics).

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“…For example, Pestian et al used NLP features and semi-supervised machine learning methods to discriminate between the conversation of suicidal and non-suicidal individuals. 7 Patel et al used NLP techniques to identify cannabis use that was documented in free text clinical records. 8 Further, Rumshisky et al used features generated from the Latent Dirichlet Allocation (LDA) model to enhance the accuracy of predicting early psychiatric readmission.…”

Section: Introductionmentioning

confidence: 99%

Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge

Zhang

et al. 2017

Journal of Biomedical Informatics

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Objective Mental health is becoming an increasingly important topic in healthcare. Psychiatric symptoms, which consist of subjective descriptions of the patient’s experience, as well as the nature and severity of mental disorders, are critical to support the phenotypic classification for personalized prevention, diagnosis, and intervention of mental disorders. However, few automated approaches have been proposed to extract psychiatric symptoms from clinical text, mainly due to (a) the lack of annotated corpora, which are time-consuming and costly to build, and (b) the inherent linguistic difficulties that symptoms present as they are not well-defined clinical concepts like diseases. The goal of this study is to investigate techniques for recognizing psychiatric symptoms in clinical text without labeled data. Instead, external knowledge in the form of publicly available “seed” lists of symptoms is leveraged using unsupervised distributional representations. Materials and Methods First, psychiatric symptoms are collected from three online repositories of healthcare knowledge for consumers—MedlinePlus, Mayo Clinic, and the American Psychiatric Association—for use as seed terms. Candidate symptoms in psychiatric notes are automatically extracted using phrasal syntax patterns. In particular, the 2016 CEGS N-GRID challenge data serves as the psychiatric note corpus. Second, three corpora—psychiatric notes, psychiatric forum data, and MIMIC II—are adopted to generate distributional representations with . Finally, semantic similarity between the distributional representations of the seed symptoms and candidate symptoms is calculated to assess the relevance of a phrase. Experiments were performed on a set of psychiatric notes from the CEGS N-GRID 2016 Challenge. Results & Conclusion Our method demonstrates good performance at extracting symptoms from an unseen corpus, including symptoms with no word overlap with the provided seed terms. Semantic similarity based on the distributional representation outperformed baseline methods. Our experiment yielded two interesting results. First, distributional representations built from social media data outperformed those built from clinical data. And second, the distributional representation model built from sentences resulted in better representations of phrases than the model built from phrase alone.

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A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department

Cited by 63 publications

References 21 publications

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Identifying epilepsy psychiatric comorbidities with machine learning

Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge

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