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Article ; Online: Reflections on measuring disordered thoughts as expressed via language.

Elvevåg, Brita

2023 Volume 322, Page(s) 115098

Abstract: Thought disorder, as inferred from disorganized and incoherent speech, is an important part of the clinical presentation in schizophrenia. Traditional measurement approaches essentially count occurrences of certain speech events which may have restricted ...

Abstract	Thought disorder, as inferred from disorganized and incoherent speech, is an important part of the clinical presentation in schizophrenia. Traditional measurement approaches essentially count occurrences of certain speech events which may have restricted their usefulness. Applying speech technologies in assessment can help automate traditional clinical rating tasks and thereby complement the process. Adopting these computational approaches affords clinical translational opportunities to enhance the traditional assessment by applying such methods remotely and scoring various parts of the assessment automatically. Further, digital measures of language may help detect subtle clinically significant signs and thus potentially disrupt the usual manner by which things are conducted. If proven beneficial to patient care, methods where patients' voice are the primary data source could become core components of future clinical decision support systems that improve risk assessment. However, even if it is possible to measure thought disorder in a sensitive, reliable and efficient manner, there remain many challenges to then translate into a clinically implementable tool that can contribute towards providing better care. Indeed, embracing technology - notably artificial intelligence - requires vigorous standards for reporting underlying assumptions so as to ensure a trustworthy and ethical clinical science.
MeSH term(s)	Humans ; Artificial Intelligence ; Language ; Schizophrenia ; Speech ; Voice
Language	English
Publishing date	2023-02-06
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.psychres.2023.115098
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Article ; Online: Translating Natural Language Processing into Mainstream Schizophrenia Assessment.

Elvevåg, Brita / Cohen, Alex S

Schizophrenia bulletin

2022 Volume 48, Issue 5, Page(s) 936–938

MeSH term(s)	Humans ; Natural Language Processing ; Schizophrenia ; Schizophrenic Psychology ; Translating
Language	English
Publishing date	2022-09-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	439173-1
ISSN	1745-1701 ; 0586-7614
ISSN (online)	1745-1701
ISSN	0586-7614
DOI	10.1093/schbul/sbac087
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Article ; Online: Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies.

Chandler, Chelsea / Foltz, Peter W / Elvevåg, Brita

Schizophrenia bulletin

2022 Volume 48, Issue 5, Page(s) 949–957

Abstract: Objectives: Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers ... ...

Abstract	Objectives: Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process. Methods: We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach. Results: Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy. Conclusions: Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model's accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.
MeSH term(s)	Artificial Intelligence ; Humans ; Machine Learning ; Natural Language Processing ; Psychiatry ; Reproducibility of Results
Language	English
Publishing date	2022-06-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	439173-1
ISSN	1745-1701 ; 0586-7614
ISSN (online)	1745-1701
ISSN	0586-7614
DOI	10.1093/schbul/sbac038
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Article ; Online: The mental health consequences on children of the war in Ukraine: A commentary.

Elvevåg, Brita / DeLisi, Lynn E

Psychiatry research

2022 Volume 317, Page(s) 114798

Abstract: The news from Ukraine is currently full of heart-wrenching stories accompanied by graphic images of civilian casualties and massacres that are telecast world-wide on a daily basis. It is hard to fathom the magnitude of the devastation and disruption to ... ...

Abstract	The news from Ukraine is currently full of heart-wrenching stories accompanied by graphic images of civilian casualties and massacres that are telecast world-wide on a daily basis. It is hard to fathom the magnitude of the devastation and disruption to regular lives and everyday routines that war brings with it, the witnessing of countless deaths, the associated trauma of living in perpetual fear, and the daily experience of many families and orphans who are crowded into basement bomb shelters now for months on end. These issues make us contemplate the mental health consequences, among other lasting effects, of this costly war in Ukraine, and wars in other countries not so widely featured in Western news. Despite people of all ages being affected by war, children are especially vulnerable. This commentary outlines some of the epidemiology of the consequences of war, the mental health sequelae specifically, and the complexity of providing culturally and contextually relevant interventions that meet the needs of children.
MeSH term(s)	Humans ; Child ; Mental Health ; Ukraine/epidemiology ; Bombs
Language	English
Publishing date	2022-08-18
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.psychres.2022.114798
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Article ; Online: A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions.

Diaz-Asper, Catherine / Hauglid, Mathias K / Chandler, Chelsea / Cohen, Alex S / Foltz, Peter W / Elvevåg, Brita

The American psychologist

2024 Volume 79, Issue 1, Page(s) 79–91

Abstract: Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about ...

Abstract	Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
MeSH term(s)	Humans ; Behavioral Research ; Artificial Intelligence ; Language ; Technology ; Communication
Language	English
Publishing date	2024-01-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	209464-2
ISSN	1935-990X ; 0003-066X
ISSN (online)	1935-990X
ISSN	0003-066X
DOI	10.1037/amp0001195
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Article: An explainable machine learning model of cognitive decline derived from speech.

Chandler, Chelsea / Diaz-Asper, Catherine / Turner, Raymond S / Reynolds, Brigid / Elvevåg, Brita

Alzheimer's & dementia (Amsterdam, Netherlands)

2023 Volume 15, Issue 4, Page(s) e12516

Abstract: Introduction: Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only ... ...

Abstract	Introduction: Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders. Methods: Speech collected over the telephone from 91 older participants who were cognitively healthy ( Results: This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data. Discussion: This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies. Highlights: Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
Language	English
Publishing date	2023-12-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2832898-X
ISSN	2352-8729
ISSN	2352-8729
DOI	10.1002/dad2.12516
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Article ; Online: Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness.

Chandler, Chelsea / Foltz, Peter W / Elvevåg, Brita

Schizophrenia bulletin

2020 Volume 46, Issue 1, Page(s) 11–14

Abstract: The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as ... ...

Abstract	The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..
MeSH term(s)	Humans ; Machine Learning ; Models, Theoretical ; Psychiatry/methods ; Psychiatry/standards
Language	English
Publishing date	2020-01-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	439173-1
ISSN	1745-1701 ; 0586-7614
ISSN (online)	1745-1701
ISSN	0586-7614
DOI	10.1093/schbul/sbz105
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Article ; Online: The reality of doing things with (thousands of) words in applied research and clinical settings: A commentary on Clarke et al. (2020).

Holmlund, Terje B / Diaz-Asper, Catherine / Elvevåg, Brita

Cortex; a journal devoted to the study of the nervous system and behavior

2020 Volume 136, Page(s) 150–156

MeSH term(s)	Humans ; Research
Language	English
Publishing date	2020-09-12
Publishing country	Italy
Document type	Journal Article ; Comment
ZDB-ID	280622-8
ISSN	1973-8102 ; 0010-9452
ISSN (online)	1973-8102
ISSN	0010-9452
DOI	10.1016/j.cortex.2020.08.024
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Article ; Online: Celebrating the accomplishments of thought leaders in psychiatry research: Introduction.

DeLisi, Lynn E / Elvevåg, Brita / Gooding, Diane C / Park, Sohee / Schwab, Sibylle G

Psychiatry research

2022 Volume 316, Page(s) 114761

Abstract: In academia and related industry, particularly in the medical sciences, some individuals are noticed for their ability to attract others towards their ideas, theories and objectives. They are often referred to as the "thought leaders" of the field. ... ...

Abstract	In academia and related industry, particularly in the medical sciences, some individuals are noticed for their ability to attract others towards their ideas, theories and objectives. They are often referred to as the "thought leaders" of the field. Noticeably, individuals who are labeled as "thought leaders" appear more often to be males than females. Moreover, this is not a racially or ethnically diverse group. In this special issue, we intend to challenge that bias. As we look world-wide at the incredibly important contributions of women in both psychiatry and related neuroscience, it was a logical step to ask these 'thought leaders' to write commentaries on their most important work, how they got there, and what they predict for the future. When compiling a list of "thought leaders" for future academic and industry workshops, these scientists are certain to enrich and advance the discourse.
MeSH term(s)	Female ; Humans ; Leadership ; Male ; Psychiatry
Language	English
Publishing date	2022-08-05
Publishing country	Ireland
Document type	Editorial
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.psychres.2022.114761
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Article ; Online: Increasing access to cognitive screening in the elderly: Applying natural language processing methods to speech collected over the telephone.

Diaz-Asper, Catherine / Chandler, Chelsea / Turner, Raymond S / Reynolds, Brigid / Elvevåg, Brita

Cortex; a journal devoted to the study of the nervous system and behavior

2022 Volume 156, Page(s) 26–38

Abstract: Barriers to healthcare access are widespread in elderly populations, with a major consequence that older people are not benefiting from the latest technologies to diagnose disease. Recent advances in the automated analysis of speech show promising ... ...

Abstract	Barriers to healthcare access are widespread in elderly populations, with a major consequence that older people are not benefiting from the latest technologies to diagnose disease. Recent advances in the automated analysis of speech show promising results in the identification of cognitive decline associated with Alzheimer's disease (AD), as well as its purported pre-clinical stage. We utilized automated methods to analyze speech recorded over the telephone in 91 community-dwelling older adults diagnosed with mild AD, amnestic mild cognitive impairment (aMCI) or cognitively healthy. We asked whether natural language processing (NLP) and machine learning could more accurately identify groups than traditional screening tools and be sensitive to subtle differences in speech between the groups. Despite variable recording quality, NLP methods differentiated the three groups with greater accuracy than two traditional dementia screeners and a clinician who read transcripts of their speech. Imperfect speech data collected via a telephone is of sufficient quality to be examined with the latest speech technologies. Critically, these data reveal significant differences in speech that closely match the clinical diagnoses of AD, aMCI and healthy control.
MeSH term(s)	Humans ; Aged ; Speech ; Neuropsychological Tests ; Natural Language Processing ; Cognitive Dysfunction/psychology ; Alzheimer Disease/psychology ; Cognition ; Telephone
Language	English
Publishing date	2022-08-30
Publishing country	Italy
Document type	Journal Article
ZDB-ID	280622-8
ISSN	1973-8102 ; 0010-9452
ISSN (online)	1973-8102
ISSN	0010-9452
DOI	10.1016/j.cortex.2022.08.005
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