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General General

Language Analytics for Assessment of Mental Health Status and Functional Competency.

In Schizophrenia bulletin ; h5-index 79.0

BACKGROUND AND HYPOTHESIS : Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling.

STUDY DESIGN : Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer.

STUDY RESULTS : Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables.

CONCLUSIONS : Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.

Voleti Rohit, Woolridge Stephanie M, Liss Julie M, Milanovic Melissa, Stegmann Gabriela, Hahn Shira, Harvey Philip D, Patterson Thomas L, Bowie Christopher R, Berisha Visar

2023-Mar-22

bipolar disorder, machine learning, natural language processing, schizophrenia, social skills prediction, speech analysis

General General

Coreference Delays in Psychotic Discourse: Widening the Temporal Window.

In Schizophrenia bulletin ; h5-index 79.0

BACKGROUND AND HYPOTHESIS : Any form of coherent discourse depends on saying different things about the same entities at different times. Such recurrent references to the same entity need to predictably happen within certain temporal windows. We hypothesized that a failure of control over reference in speakers with schizophrenia (Sz) would become manifest through dynamic temporal measures.

STUDY DESIGN : Conversational speech with a mean of 909.2 words (SD: 178.4) from 20 Chilean Spanish speakers with chronic Sz, 20 speakers at clinical high risk (CHR), and 20 controls were collected. Using directed speech graphs with referential noun phrases (NPs) as nodes, we studied deviances in the topology and temporal distribution of such NPs and of the entities they denote over narrative time.

STUDY RESULTS : The Sz group had a larger density of NPs (number of NPs divided by total words) relative to both controls and CHR. This related to topological measures of distance between recurrent entities, which revealed that the Sz group produced more recurrences, as well as greater topological distances between them, relative to controls. A logistic regression using five topological measures showed that Sz and controls can be distinguished with 84.2% accuracy.

CONCLUSIONS : This pattern indicates a widening of the temporal window in which entities are maintained in discourse and co-referenced in it. It substantiates and extends earlier evidence for deficits in the cognitive control over linguistic reference in psychotic discourse and informs both neurocognitive models of language in Sz and machine learning-based linguistic classifiers of psychotic speech.

Palominos Claudio, Figueroa-Barra Alicia, Hinzen Wolfram

2023-Mar-22

narrative, reference, speech graphs, topological distances

General General

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.

In Schizophrenia bulletin ; h5-index 79.0

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

Corona Hernández Hugo, Corcoran Cheryl, Achim Amélie M, de Boer Janna N, Boerma Tessel, Brederoo Sanne G, Cecchi Guillermo A, Ciampelli Silvia, Elvevåg Brita, Fusaroli Riccardo, Giordano Silvia, Hauglid Mathias, van Hessen Arjan, Hinzen Wolfram, Homan Philipp, de Kloet Sybren F, Koops Sanne, Kuperberg Gina R, Maheshwari Kritika, Mota Natalia B, Parola Alberto, Rocca Roberta, Sommer Iris E C, Truong Khiet, Voppel Alban E, van Vugt Marieke, Wijnen Frank, Palaniyappan Lena

2023-Mar-22

digital markers, implementation, pathophysiology, psychiatric practice, speech technology

General General

Language and Psychosis: Tightening the Association.

In Schizophrenia bulletin ; h5-index 79.0

This special issue of DISCOURSE in Psychosis focuses on the role of language in psychosis, including the relationships between formal thought disorder and conceptual disorganization, with speech and language markers and the neural mechanisms underlying these features in psychosis. It also covers the application of computational techniques in the study of language in psychosis, as well as the potential for using speech and language data for digital phenotyping in psychiatry.

Tan Eric J, Sommer Iris E C, Palaniyappan Lena

2023-Mar-22

artificial intelligence, clinical trials, discourse, linguistics, phenomenology, psycholinguistics, psychopathology

General General

Drug Repurposing for viral cancers: A paradigm of machine learning, deep learning, and Virtual screening-based approaches.

In Journal of medical virology

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the FDA approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided. This article is protected by copyright. All rights reserved.

Ahmed Faheem, Kang In Suk, Kim Kyung Hwan, Asif Arun, Rahim Chethikkattuveli Salih Abdul, Samantasinghar Anupama, Memon Fida Hussain, Choi Kyung Hyun

2023-Mar-22

Anti-hepatitis B virus antivirals, Antiviral agents, Antiviral agentsAntivir, al agentsAntiviral agentsAntiv, iral agentsAntiviral agentsAntiviral agentsArtificial intelligenceBiostatistics & BioinformaticsRational drug design

Pathology Pathology

Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD): Study Design and Protocol.

In The journal of prevention of Alzheimer's disease

BACKGROUND : Speech impairments are an early feature of Alzheimer's disease (AD) and consequently, analysing speech performance is a promising new digital biomarker for AD screening. Future clinical AD trials on disease modifying drugs will require a shift to very early identification of individuals at risk of dementia. Hence, digital markers of language and speech may offer a method for screening of at-risk populations that are at the earliest stages of AD, eventually in combination with advanced machine learning. To this end, we developed a screening battery consisting of speech-based neurocognitive tests. The automated test performs a remote primary screening using a simple telephone.

OBJECTIVES : PROSPECT-AD aims to validate speech biomarkers for identification of individuals with early signs of AD and monitor their longitudinal course through access to well-phenotyped cohorts.

DESIGN : PROSPECT-AD leverages ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding a collection of speech data over the telephone to existing longitudinal follow-ups. Participants at risk of dementia are recruited from existing parent cohorts across Europe to form an AD 'probability-spectrum', i.e., individuals with a low risk to high risk of developing AD dementia. The characterization of cognition, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide a well-phenotyped population for comparing novel speech markers with current gold standard biomarkers and cognitive scores.

PARTICIPANTS : N= 1000 participants aged 50 or older will be included in total, with a clinical dementia rating scale (CDR) score of 0 or 0.5. The study protocol is planned to run according to sites between 12 and 18 months.

MEASUREMENTS : The speech protocol includes the following neurocognitive tests which will be administered remotely: Word List [Memory Function], Verbal Fluency [Executive Functions] and spontaneous free speech [Psychological and/ or behavioral symptoms]. Speech features on the linguistic and paralinguistic level will be extracted from the recordings and compared to data from CSF and blood biomarkers, neuroimaging, neuropsychological evaluations, genetic profiles, and family history. Primary candidate marker from speech will be a combination of most significant features in comparison to biomarkers as reference measure. Machine learning and computational techniques will be employed to identify the most significant speech biomarkers that could represent an early indicator of AD pathology. Furthermore, based on the analysis of speech performances, models will be trained to predict cognitive decline and disease progression across the AD continuum.

CONCLUSION : The outcome of PROSPECT-AD may support AD drug development research as well as primary or tertiary prevention of dementia by providing a validated tool using a remote approach for identifying individuals at risk of dementia and monitoring individuals over time, either in a screening context or in clinical trials.

König A, Linz N, Baykara E, Tröger J, Ritchie C, Saunders S, Teipel S, Köhler S, Sánchez-Benavides G, Grau-Rivera O, Gispert J D, Palmqvist S, Tideman P, Hansson O

2023

Alzheimer’s disease, Dementia, cognitive assessment, machine learning, phone-based, screening, speech biomarker