Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech

Badal, Varsha D.; Depp, Colin A.; Shinkawa, Kaoru; Yamada, Yasunori; Palinkas, Lawrence A.; Kim, Ho-Cheol; Jeste, Dilip V.; Lee, Ellen

doi:10.1016/j.jagp.2020.09.009

Cited by 39 publications

(40 citation statements)

References 41 publications

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“…By evaluating uses for AI and implications in health care, veteran input and priorities can be proactively developed to enhance care. For example, the VA is already using AI to facilitate early detection of cancer [ 3 ], detection of acute kidney injury [ 6 ], and prediction of loneliness and declines in mental health [ 7 , 8 ]. These examples all highlight the ways that AI can be used to advance patient outcomes; however, they also point toward data privacy and trust considerations.…”

Section: Ethical Applications Of Artificial Intelligence In Veterans’ Health Researchmentioning

confidence: 99%

“…AI, implemented along with smart devices (eg, smart wearables), could allow for remote monitoring of rural veterans’ health and enable smart devices to alert veterans of health concerns. Recent evidence indicates that AI may be able to predict a person’s mental state, including the likelihood of suicide, raising the likelihood that smart devices could be used for predicting and intervening in veteran suicide [ 7 , 8 ].…”

Section: Purposeful and Performance Drivenmentioning

confidence: 99%

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Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

Makridis¹,

hurley²,

Klote³

et al. 2021

JMIR Med Inform

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Background Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans. Objective We explore the newly developed principles around trustworthy AI and how they can be readily applied at scale to vulnerable groups that are potentially less likely to benefit from technological advances. Methods Using the US Department of Veterans Affairs as a case study, we explore the principles of trustworthy AI that are of particular interest for vulnerable groups and veterans. Results We focus on three principles: (1) designing, developing, acquiring, and using AI so that the benefits of its use significantly outweigh the risks and the risks are assessed and managed; (2) ensuring that the application of AI occurs in well-defined domains and is accurate, effective, and fit for the intended purposes; and (3) ensuring that the operations and outcomes of AI applications are sufficiently interpretable and understandable by all subject matter experts, users, and others. Conclusions These principles and applications apply more generally to vulnerable groups, and adherence to them can allow the VA and other organizations to continue modernizing their technology governance, leveraging the gains of AI while simultaneously managing its risks.

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Section: Ethical Applications Of Artificial Intelligence In Veterans’ Health Researchmentioning

confidence: 99%

Section: Purposeful and Performance Drivenmentioning

confidence: 99%

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

Makridis¹,

hurley²,

Klote³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

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“…Many of such studies employ machine learning techniques to detect loneliness through classification of collected naturally occurring data. Alternate approaches have employed machine learning to detect loneliness using the data collected by questionnaires, such as demographic information and health related information (Yang & Bath, 2018), or audio data, to analyse speech and recognise sentiment (Badal et al, 2020).…”

Section: Detecting Loneliness With Digital Technology and Data Sciencementioning

confidence: 99%

Loneliness in Older People and COVID-19: Applying the Social Identity Approach to Digital Intervention Design

Stuart¹,

Katz²,

Stevenson³

et al. 2021

Preprint

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The COVID-19 pandemic is increasing older people's existing challenges in engaging with their physical and social worlds, and is thereby likely to worsen their loneliness. Digital technology has been offered as a potential aid for social connectedness during social distancing/isolation. However, many popular digital communication tools have not been designed to specifically address the needs of older adults impacted by social isolation. We propose that the social identity approach to health and the Social Identity Model of Identity Change (SIMIC) could be a foundation for digital interventions to address loneliness. While SIMIC applies to maintaining wellbeing during life transitions, it has not previously been rigorously applied to digital interventions. There are known challenges to integrating psychological theory to the design of digital technology, such as efficacy, user-autonomy, and engagement. The interdisciplinary field of Human Computer Interaction has a history of drawing on models originating from psychology to improve the design of digital technology and to design technologies in an appropriate manner. Drawing on key lessons from this literature, we consolidate design guidelines that could assist in applying SIMIC to digital interventions for loneliness in older people affected by the pandemic.

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“…In schizophrenia, NLP has shown tangential and concrete speech and reductions in semantic coherence and syntactic complexity [40]. In elder people, NLP could predict loneliness with high precision by analyzing audio-taped interviews [41]. NLP has also been used to predict therapeutic alliance analyzing psychotherapy sessions recordings [42], for detecting risk of school violence [43], or for screening suicide risk assessing social media posts [44].…”

Section: Related Workmentioning

confidence: 99%

Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms

Toledo-Acosta

Barreiro²,

Alamillo³

et al. 2020

Mathematics

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Mathematical modeling of language in Artificial Intelligence is of the utmost importance for many research areas and technological applications. Over the last decade, research on text representation has been directed towards the investigation of dense vectors popularly known as word embeddings. In this paper, we propose a cognitive-emotional scoring and representation framework for text based on word embeddings. This representation framework aims to mathematically model the emotional content of words in short free-form text messages, produced by adults in follow-up due to any mental health condition in the outpatient facilities within the Psychiatry Department of Hospital Fundación Jiménez Díaz in Madrid, Spain. Our contribution is a geometrical-topological framework for Sentiment Analysis, that includes a hybrid method that uses a cognitively-based lexicon together with word embeddings to generate graded sentiment scores for words, and a new topological method for clustering dense vector representations in high-dimensional spaces, where points are very sparsely distributed. Our framework is useful in detecting word association topics, emotional scoring patterns, and embedded vectors’ geometrical behavior, which might be useful in understanding language use in this kind of texts. Our proposed scoring system and representation framework might be helpful in studying relations between language and behavior and their use might have a predictive potential to prevent suicide.

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Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech

Cited by 39 publications

References 41 publications

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

Loneliness in Older People and COVID-19: Applying the Social Identity Approach to Digital Intervention Design

Cognitive Emotional Embedded Representations of Text to Predict Suicidal Ideation and Psychiatric Symptoms

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