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Article: Why use pre-differentiated cells to address complex multi-factorial neurodegenerative diseases?

Kopyov, Alex / Uhlendorf, Toni L / Cohen, Randy W

2020 Volume 16, Issue 7, Page(s) 1413–1414

Language	English
Publishing date	2020-12-15
Publishing country	India
Document type	Journal Article
ZDB-ID	2388460-5
ISSN	1876-7958 ; 1673-5374
ISSN (online)	1876-7958
ISSN	1673-5374
DOI	10.4103/1673-5374.300990
Database	MEDical Literature Analysis and Retrieval System OnLINE

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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 ; Online: Evidence-based post-ban research to inform effective menthol cigarette bans in the United States and other jurisdictions.

Erinoso, Olufemi / Brown, Jennifer L / Glasser, Allison M / Gravely, Shannon / Fong, Geoffrey T / Chung-Hall, Janet / Kyriakos, Christina N / Liber, Alex C / Craig, Lorraine V / White, Augustus M / Rose, Shyanika W / Smiley, Sabrina L / Zeller, Mitch / Leischow, Scott / Ayo-Yusuf, Olalekan / Cohen, Joanna E / Ashley, David

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco

2024

Language	English
Publishing date	2024-04-13
Publishing country	England
Document type	Journal Article
ZDB-ID	1452315-2
ISSN	1469-994X ; 1462-2203
ISSN (online)	1469-994X
ISSN	1462-2203
DOI	10.1093/ntr/ntae082
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Zs.A 5789: Show issues

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Article: Using Automated Speech Processing for Repeated Measurements in a Clinical Setting of the Behavioral Variability in the Stroop Task.

Holmlund, Terje B / Cohen, Alex S / Cheng, Jian / Foltz, Peter W / Bernstein, Jared / Rosenfeld, Elizabeth / Laeng, Bruno / Elvevåg, Brita

Brain sciences

2023 Volume 13, Issue 3

Abstract: The Stroop interference task is indispensable to current neuropsychological practice. Despite this, it is limited in its potential for repeated administration, its sensitivity and its demands on professionals and their clients. We evaluated a digital ... ...

Abstract	The Stroop interference task is indispensable to current neuropsychological practice. Despite this, it is limited in its potential for repeated administration, its sensitivity and its demands on professionals and their clients. We evaluated a digital Stroop deployed using a smart device. Spoken responses were timed using automated speech recognition. Participants included adult nonpatients (N = 113; k = 5 sessions over 5 days) and patients with psychiatric diagnoses (N = 85; k = 3-4 sessions per week over 4 weeks). Traditional interference (difference in response time between color incongruent words vs. color neutral words; M = 0.121 s) and facilitation (neutral vs. color congruent words; M = 0.085 s) effects were robust and temporally stable over testing sessions (ICCs 0.50-0.86). The performance showed little relation to clinical symptoms for a two-week window for either nonpatients or patients but was related to self-reported concentration at the time of testing for both groups. Performance was also related to treatment outcomes in patients. The duration of response word utterances was longer in patients than in nonpatients. Measures of intra-individual variability showed promise for understanding clinical state and treatment outcome but were less temporally stable than measures based solely on average response time latency. This framework of remote assessment using speech processing technology enables the fine-grained longitudinal charting of cognition and verbal behavior. However, at present, there is a problematic lower limit to the absolute size of the effects that can be examined when using voice in such a brief 'out-of-the-laboratory condition' given the temporal resolution of the speech-to-text detection system (in this case, 10 ms). This resolution will limit the parsing of meaningful effect sizes.
Language	English
Publishing date	2023-03-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651993-8
ISSN	2076-3425
ISSN	2076-3425
DOI	10.3390/brainsci13030442
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Book ; Online: Bridging the Skills Gap

Kearns, William R. / Bertram, Jessica / Divina, Myra / Kemp, Lauren / Wang, Yinzhou / Marin, Alex / Cohen, Trevor / Yuwen, Weichao

Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy

2024

Abstract: ... AMIA Annual Symposium 2023. To appear as: Kearns W, Bertram J, Divina M, Kemp L, Wang Y, Marin A, Cohen ... T, Yuwen W. Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care ...

Abstract	Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI) methods have been proposed as a way to address this shortage, by supporting providers with less extensive training as they deliver care. To this end, we developed the AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response suggestion feature, which supports providers in delivering protocolized therapies empathetically. We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features. Upon evaluation, the AI-assisted system significantly decreased response times by 29.34% (p=0.002), tripled empathic response accuracy (p=0.0001), and increased goal recommendation accuracy by 66.67% (p=0.001) across both user groups compared to the control. Both groups rated the system as having excellent usability. Comment: Accepted: AMIA Annual Symposium 2023. To appear as: Kearns W, Bertram J, Divina M, Kemp L, Wang Y, Marin A, Cohen T, Yuwen W. Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy. AMIA Annual Symposium Proceedings 2023. American Medical Informatics Association
Keywords	Computer Science - Human-Computer Interaction ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Information Retrieval ; Computer Science - Machine Learning
Subject code	004
Publishing date	2024-01-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Using automated syllable counting to detect missing information in speech transcripts from clinical settings.

Diaz-Asper, Marama / Holmlund, Terje B / Chandler, Chelsea / Diaz-Asper, Catherine / Foltz, Peter W / Cohen, Alex S / Elvevåg, Brita

Psychiatry research

2022 Volume 315, Page(s) 114712

Abstract: Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality ... ...

Abstract	Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.
MeSH term(s)	Humans ; Phonetics ; Speech ; Speech Perception ; Speech Production Measurement
Language	English
Publishing date	2022-07-05
Publishing country	Ireland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
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.2022.114712
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Article ; Online: Extending the usefulness of the verbal memory test: The promise of machine learning.

Chandler, Chelsea / Holmlund, Terje B / Foltz, Peter W / Cohen, Alex S / Elvevåg, Brita

Psychiatry research

2021 Volume 297, Page(s) 113743

Abstract: The evaluation of verbal memory is a core component of neuropsychological assessment in a wide range of clinical and research settings. Leveraging story recall to assay neurocognitive function could be made more useful if it were possible to administer ... ...

Abstract	The evaluation of verbal memory is a core component of neuropsychological assessment in a wide range of clinical and research settings. Leveraging story recall to assay neurocognitive function could be made more useful if it were possible to administer frequently (i.e., would allow for the collection of more patient data over time) and automatically assess the recalls with machine learning methods. In the present study, we evaluated a novel story recall test with 24 parallel forms that was deployed using smart devices in 94 psychiatric inpatients and 80 nonpatient adults. Machine learning and vector-based natural language processing methods were employed to automate test scoring, and performance using these methods was evaluated in their incremental validity, criterion validity (i.e., convergence with trained human raters), and parallel forms reliability. Our results suggest moderate to high consistency across the parallel forms, high convergence with human raters (r values ~ 0.89), and high incremental validity for discriminating between groups. While much work remains, the present findings are critical for implementing an automated, neuropsychological test deployable using remote technologies across multiple and frequent administrations.
MeSH term(s)	Adult ; Humans ; Machine Learning ; Memory ; Mental Recall ; Neuropsychological Tests ; Reproducibility of Results ; Verbal Learning
Language	English
Publishing date	2021-01-19
Publishing country	Ireland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
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.2021.113743
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Article: FEAR index in predicting treatment among patients with femoroacetabular impingement and hip dysplasia and the relationship of femoral version.

Meyer, Alex M / Schaver, Andrew L / Cohen, Brian H / Glass, Natalie A / Willey, Michael C / Westermann, Robert W

Journal of hip preservation surgery

2022 Volume 9, Issue 2, Page(s) 84–89

Abstract: The Femoro-Epiphyseal Acetabular Roof (FEAR) index is a newer measurement to identify the hip instability with borderline acetabular dysplasia. The purpose of this study is to (i) validate the FEAR index in determining the stability of the hip in ... ...

Abstract	The Femoro-Epiphyseal Acetabular Roof (FEAR) index is a newer measurement to identify the hip instability with borderline acetabular dysplasia. The purpose of this study is to (i) validate the FEAR index in determining the stability of the hip in patients who have previously been treated surgically for femoroacetabular impingement (FAI) and/or developmental dysplasia of the hip (DDH) and (ii) to examine the relationship between the FEAR index and femoral version, lateral center edge angle, Tönnis angle and alpha angle (AA). Patient demographics and radiographic measurements of 215 hips (178 patients), 116 hips treated with hip arthroscopy for FAI and 99 hips treated with periacetabular osteotomy (PAO) for DDH were compared between groups. The sensitivity and specificity of the FEAR index to detect the surgical procedure performed (PAO or hip arthroscopy) was calculated, and a threshold value was proposed. Pearson's correlation coefficients were used to describe the relationships between the FEAR index, femoral version and other radiographic measurements. The FEAR index was higher in patients with DDH versus FAI (DDH: 2.81 ± 0.50° versus FAI: -1.00 ± 0.21°,
Language	English
Publishing date	2022-04-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2773022-0
ISSN	2054-8397
ISSN	2054-8397
DOI	10.1093/jhps/hnac023
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Article ; Online: Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors.

Holmlund, Terje B / Chandler, Chelsea / Foltz, Peter W / Diaz-Asper, Catherine / Cohen, Alex S / Rodriguez, Zachary / Elvevåg, Brita

Schizophrenia research

2022 Volume 259, Page(s) 71–79

Abstract: Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken ... ...

Abstract	Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.
MeSH term(s)	Humans ; Semantics ; Speech ; Language ; Cognition ; Speech Perception
Language	English
Publishing date	2022-11-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639422-x
ISSN	1573-2509 ; 0920-9964
ISSN (online)	1573-2509
ISSN	0920-9964
DOI	10.1016/j.schres.2022.09.020
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Zs.A 2351: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 543: Show issues

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Article ; Online: Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function.

Foltz, Peter W / Chandler, Chelsea / Diaz-Asper, Catherine / Cohen, Alex S / Rodriguez, Zachary / Holmlund, Terje B / Elvevåg, Brita

Schizophrenia research

2022 Volume 259, Page(s) 127–139

Abstract: Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. ... ...

Abstract	Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.
MeSH term(s)	Humans ; Cognition ; Language
Language	English
Publishing date	2022-09-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	639422-x
ISSN	1573-2509 ; 0920-9964
ISSN (online)	1573-2509
ISSN	0920-9964
DOI	10.1016/j.schres.2022.07.011
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