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Trifu RN, Nemeș B, Herta DC, Bodea-Hategan C, Talaș DA, Coman H. Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure. Front Psychol 2024; 15:1355734. [PMID: 38510303 PMCID: PMC10953917 DOI: 10.3389/fpsyg.2024.1355734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts. Aim This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count). Materials and methods A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them. Assessment tools (1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use. Results Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money. Conclusion Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.
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Affiliation(s)
- Raluca Nicoleta Trifu
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Bogdan Nemeș
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Dana Cristina Herta
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Carolina Bodea-Hategan
- Special Education Department, Faculty of Psychology and Education Sciences, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Dorina Anca Talaș
- Special Education Department, Faculty of Psychology and Education Sciences, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Horia Coman
- Department of Neurosciences, Discipline of Medical Psychology and Psychiatry, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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Seyedi S, Griner E, Corbin L, Jiang Z, Roberts K, Iacobelli L, Milloy A, Boazak M, Bahrami Rad A, Abbasi A, Cotes RO, Clifford GD. Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study. JMIR Ment Health 2023; 10:e48517. [PMID: 37906217 PMCID: PMC10646674 DOI: 10.2196/48517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.
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Affiliation(s)
- Salman Seyedi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Emily Griner
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Lisette Corbin
- Department of Psychiatry, Duke University Health, Durham, NC, United States
| | - Zifan Jiang
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Kailey Roberts
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Luca Iacobelli
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Aaron Milloy
- Infection Prevention Department, Emory Healthcare, Atlanta, GA, United States
| | - Mina Boazak
- Animo Sano Psychiatry, Durham, NC, United States
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Ahmed Abbasi
- Department of Information Technology, Analytics, and Operations, University of Notre Dame, Notre Dame, IN, United States
| | - Robert O Cotes
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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Jagfeld G, Lobban F, Davies R, Boyd RL, Rayson P, Jones S. Posting patterns in peer online support forums and their associations with emotions and mood in bipolar disorder: Exploratory analysis. PLoS One 2023; 18:e0291369. [PMID: 37747891 PMCID: PMC10519601 DOI: 10.1371/journal.pone.0291369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 08/26/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Mental health (MH) peer online forums offer robust support where internet access is common, but healthcare is not, e.g., in countries with under-resourced MH support, rural areas, and during pandemics. Despite their widespread use, little is known about who posts in such forums, and in what mood states. The discussion platform Reddit is ideally suited to study this as it hosts forums (subreddits) for MH and non-MH topics. In bipolar disorder (BD), where extreme mood states are core defining features, mood influences are particularly relevant. OBJECTIVES This exploratory study investigated posting patterns of Reddit users with a self-reported BD diagnosis and the associations between posting and emotions, specifically: 1) What proportion of the identified users posts in MH versus non-MH subreddits? 2) What differences exist in the emotions that they express in MH or non-MH subreddit posts? 3) How does mood differ between those users who post in MH subreddits compared to those who only post in non-MH subreddits? METHODS Reddit users were automatically identified via self-reported BD diagnosis statements and all their 2005-2019 posts were downloaded. First, the percentages of users who posted only in MH (non-MH) subreddits were calculated. Second, affective vocabulary use was compared in MH versus non-MH subreddits by measuring the frequency of words associated with positive emotions, anxiety, sadness, anger, and first-person singular pronouns via the LIWC text analysis tool. Third, a logistic regression distinguished users who did versus did not post in MH subreddits, using the same LIWC variables (measured from users' non-MH subreddit posts) as predictors, controlling for age, gender, active days, and mean posts/day. RESULTS 1) Two thirds of the identified 19,685 users with a self-reported BD diagnosis posted in both MH and non-MH subreddits. 2) Users who posted in both MH and non-MH subreddits exhibited less positive emotion but more anxiety and sadness and used more first-person singular pronouns in their MH subreddit posts. 3) Feminine gender, higher positive emotion, anxiety, and sadness were significantly associated with posting in MH subreddits. CONCLUSIONS Many Reddit users who disclose a BD diagnosis use a single account to discuss MH and other concerns. Future work should determine whether users exhibit more anxiety and sadness in their MH subreddit posts because they more readily post in MH subreddits when experiencing lower mood or because they feel more able to express negative emotions in these spaces. MH forums may reflect the views of people who experience more extreme mood (outside of MH subreddits) compared to people who do not post in MH subreddits. These findings can be useful for MH professionals to discuss online forums with their clients. For example, they may caution them that forums may underrepresent people living well with BD.
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Affiliation(s)
- Glorianna Jagfeld
- Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Fiona Lobban
- Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Robert Davies
- Department of Psychology, Lancaster University, Lancaster, United Kingdom
| | - Ryan L. Boyd
- Department of Psychology, Lancaster University, Lancaster, United Kingdom
- Security Lancaster, Lancaster University, Lancaster, United Kingdom
- Data Science Institute, Lancaster University, Lancaster, United Kingdom
- Obelus Institute, Behavioral Science Division, Washington D.C., United States of America
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States of America
| | - Paul Rayson
- UCREL Research Centre, School of Computing and Communications, Lancaster University, Lancaster, United Kingdom
| | - Steven Jones
- Spectrum Centre for Mental Health Research, Division of Health Research, Lancaster University, Lancaster, United Kingdom
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Zolnoori M, Zolnour A, Topaz M. ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia. Artif Intell Med 2023; 143:102624. [PMID: 37673583 PMCID: PMC10483114 DOI: 10.1016/j.artmed.2023.102624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 06/22/2023] [Accepted: 07/08/2023] [Indexed: 09/08/2023]
Abstract
Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.
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Affiliation(s)
- Maryam Zolnoori
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America.
| | - Ali Zolnour
- School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Maxim Topaz
- Columbia University Medical Center, New York, NY, United States of America; School of Nursing, Columbia University, New York, NY, United States of America
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Stamatis CA, Meyerhoff J, Liu T, Sherman G, Wang H, Liu T, Curtis B, Ungar LH, Mohr DC. Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety. Depress Anxiety 2022; 39:794-804. [PMID: 36281621 PMCID: PMC9729432 DOI: 10.1002/da.23286] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety. METHODS Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types. RESULTS We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: -.10 to -.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: .12-.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: .12-.13, ps < .05). CONCLUSION Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.
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Affiliation(s)
- Caitlin A. Stamatis
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Jonah Meyerhoff
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Tingting Liu
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Garrick Sherman
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Harry Wang
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tony Liu
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- RobloxSan MateoCaliforniaUSA
| | - Brenda Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Lyle H. Ungar
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David C. Mohr
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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Homan S, Gabi M, Klee N, Bachmann S, Moser AM, Duri' M, Michel S, Bertram AM, Maatz A, Seiler G, Stark E, Kleim B. Linguistic features of suicidal thoughts and behaviors: A systematic review. Clin Psychol Rev 2022; 95:102161. [DOI: 10.1016/j.cpr.2022.102161] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 03/28/2022] [Accepted: 04/27/2022] [Indexed: 12/13/2022]
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Rook L, Mazza MC, Lefter I, Brazier F. Toward Linguistic Recognition of Generalized Anxiety Disorder. Front Digit Health 2022; 4:779039. [PMID: 35493530 PMCID: PMC9051024 DOI: 10.3389/fdgth.2022.779039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 03/21/2022] [Indexed: 11/21/2022] Open
Abstract
Background Generalized anxiety disorder (GAD) refers to extreme, uncontrollable, and persistent worry and anxiety. The disorder is known to affect the social functioning and well-being of millions of people, but despite its prevalence and burden to society, it has proven difficult to identify unique behavioral markers. Interestingly, the worrying behavior observed in GAD is argued to stem from a verbal linguistic process. Therefore, the aim of the present study was to investigate if GAD can be predicted from the language people use to put their anxious worries into words. Given the importance of avoidance sensitivity (a higher likelihood to respond anxiously to novel or unexpected triggers) in GAD, this study also explored if prediction accuracy increases when individual differences in behavioral avoidance and approach sensitivity are taken into account. Method An expressive writing exercise was used to explore whether GAD can be predicted from linguistic characteristics of written narratives. Specifically, 144 undergraduate student participants were asked to recall an anxious experience during their university life, and describe this experience in written form. Clinically validated behavioral measures for GAD and self-reported sensitivity in behavioral avoidance/inhibition (BIS) and behavioral approach (BAS), were collected. A set of classification experiments was performed to evaluate GAD predictability based on linguistic features, BIS/BAS scores, and a concatenation of the two. Results The classification results show that GAD can, indeed, be successfully predicted from anxiety-focused written narratives. Prediction accuracy increased when differences in BIS and BAS were included, which suggests that, under those conditions, negatively valenced emotion words and words relating to social processes could be sufficient for recognition of GAD. Conclusions Undergraduate students with a high GAD score can be identified based on their written recollection of an anxious experience during university life. This insight is an important first step toward development of text-based digital health applications and technologies aimed at remote screening for GAD. Future work should investigate the extent to which these results uniquely apply to university campus populations or generalize to other demographics.
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Badal VD, Nebeker C, Shinkawa K, Yamada Y, Rentscher KE, Kim HC, Lee EE. Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing. Front Psychiatry 2021; 12:728732. [PMID: 34867518 PMCID: PMC8635064 DOI: 10.3389/fpsyt.2021.728732] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/08/2021] [Indexed: 01/13/2023] Open
Abstract
Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.
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Affiliation(s)
- Varsha D Badal
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States.,Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, United States
| | | | | | - Kelly E Rentscher
- Cousins Center for Psychoneuroimmunology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, CA, United States
| | - Ellen E Lee
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States.,Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States.,VA San Diego Healthcare System, La Jolla, CA, United States
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