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Wang N, Goel S, Ibrahim S, Badal VD, Depp C, Bilal E, Subbalakshmi K, Lee E. Decoding loneliness: Can explainable AI help in understanding language differences in lonely older adults? Psychiatry Res 2024; 339:116078. [PMID: 39003802 DOI: 10.1016/j.psychres.2024.116078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/17/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024]
Abstract
STUDY OBJECTIVES Loneliness impacts the health of many older adults, yet effective and targeted interventions are lacking. Compared to surveys, speech data can capture the personalized experience of loneliness. In this proof-of-concept study, we used Natural Language Processing to extract novel linguistic features and AI approaches to identify linguistic features that distinguish lonely adults from non-lonely adults. METHODS Participants completed UCLA loneliness scales and semi-structured interviews (sections: social relationships, loneliness, successful aging, meaning/purpose in life, wisdom, technology and successful aging). We used the Linguistic Inquiry and Word Count (LIWC-22) program to analyze linguistic features and built a classifier to predict loneliness. Each interview section was analyzed using an explainable AI (XAI) model to classify loneliness. RESULTS The sample included 97 older adults (age 66-101 years, 65 % women). The model had high accuracy (Accuracy: 0.889, AUC: 0.8), precision (F1: 0.8), and recall (1.0). The sections on social relationships and loneliness were most important for classifying loneliness. Social themes, conversational fillers, and pronoun usage were important features for classifying loneliness. CONCLUSIONS XAI approaches can be used to detect loneliness through the analyses of unstructured speech and to better understand the experience of loneliness.
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Affiliation(s)
- Ning Wang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
| | - Sanchit Goel
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, United States
| | - Stephanie Ibrahim
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
| | - Varsha D Badal
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
| | - Colin Depp
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States
| | - Erhan Bilal
- IBM Research-Yorktown, New York, United States
| | - Koduvayur Subbalakshmi
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Ellen Lee
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; VA San Diego Healthcare System, San Diego, CA, United States; Desert-Pacific Mental Illness Research Education and Clinical Center, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States.
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Li Z, Zhang D. How does the human brain process noisy speech in real life? Insights from the second-person neuroscience perspective. Cogn Neurodyn 2024; 18:371-382. [PMID: 38699619 PMCID: PMC11061069 DOI: 10.1007/s11571-022-09924-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/20/2022] [Accepted: 12/19/2022] [Indexed: 01/07/2023] Open
Abstract
Comprehending speech with the existence of background noise is of great importance for human life. In the past decades, a large number of psychological, cognitive and neuroscientific research has explored the neurocognitive mechanisms of speech-in-noise comprehension. However, as limited by the low ecological validity of the speech stimuli and the experimental paradigm, as well as the inadequate attention on the high-order linguistic and extralinguistic processes, there remains much unknown about how the brain processes noisy speech in real-life scenarios. A recently emerging approach, i.e., the second-person neuroscience approach, provides a novel conceptual framework. It measures both of the speaker's and the listener's neural activities, and estimates the speaker-listener neural coupling with regarding of the speaker's production-related neural activity as a standardized reference. The second-person approach not only promotes the use of naturalistic speech but also allows for free communication between speaker and listener as in a close-to-life context. In this review, we first briefly review the previous discoveries about how the brain processes speech in noise; then, we introduce the principles and advantages of the second-person neuroscience approach and discuss its implications to unravel the linguistic and extralinguistic processes during speech-in-noise comprehension; finally, we conclude by proposing some critical issues and calls for more research interests in the second-person approach, which would further extend the present knowledge about how people comprehend speech in noise.
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Affiliation(s)
- Zhuoran Li
- Department of Psychology, School of Social Sciences, Tsinghua University, Room 334, Mingzhai Building, Beijing, 100084 China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, 100084 China
| | - Dan Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Room 334, Mingzhai Building, Beijing, 100084 China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, 100084 China
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Alexopoulos GS. Artificial Intelligence in Geriatric Psychiatry Through the Lens of Contemporary Philosophy. Am J Geriatr Psychiatry 2024; 32:293-299. [PMID: 37813788 DOI: 10.1016/j.jagp.2023.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 09/04/2023] [Indexed: 10/11/2023]
Affiliation(s)
- George S Alexopoulos
- SP Tobin and AM Cooper Professor Emeritus (GSA), DeWitt Wallace Distinguished Scholar, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY.
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Prabhu D, Kholghi M, Sandhu M, Lu W, Packer K, Higgins L, Silvera-Tawil D. Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey. SENSORS (BASEL, SWITZERLAND) 2022; 22:9944. [PMID: 36560312 PMCID: PMC9781772 DOI: 10.3390/s22249944] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Social isolation (SI) and loneliness are 'invisible enemies'. They affect older people's health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.
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Affiliation(s)
- Deepa Prabhu
- Correspondence: (D.P.); (M.K.); Tel.: +61-4-1599-0836 (D.P.); +61-7-3253-3689 (M.K.)
| | - Mahnoosh Kholghi
- Correspondence: (D.P.); (M.K.); Tel.: +61-4-1599-0836 (D.P.); +61-7-3253-3689 (M.K.)
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Liu T, Ungar LH, Curtis B, Sherman G, Yadeta K, Tay L, Eichstaedt JC, Guntuku SC. Head versus heart: social media reveals differential language of loneliness from depression. NPJ MENTAL HEALTH RESEARCH 2022; 1:16. [PMID: 38609477 PMCID: PMC10955894 DOI: 10.1038/s44184-022-00014-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/12/2022] [Indexed: 04/14/2024]
Abstract
We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).
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Affiliation(s)
- Tingting Liu
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA.
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA.
| | - Lyle H Ungar
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Brenda Curtis
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Garrick Sherman
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Kenna Yadeta
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Louis Tay
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Johannes C Eichstaedt
- Department of Psychology, Institute for Human-Centered A.I., Stanford University, Stanford, CA, USA
| | - Sharath Chandra Guntuku
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
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Natural language processing of medical records: new understanding of suicide ideation by dementia subtypes. Int Psychogeriatr 2022; 34:319-321. [PMID: 35538873 DOI: 10.1017/s1041610222000333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Yamada Y, Shinkawa K, Nemoto M, Arai T. Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions. Front Psychiatry 2021; 12:712251. [PMID: 34966297 PMCID: PMC8710612 DOI: 10.3389/fpsyt.2021.712251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R 2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.
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Affiliation(s)
| | | | - Miyuki Nemoto
- Dementia Medical Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Tetsuaki Arai
- Division of Clinical Medicine, Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
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