Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

Argôlo, Felipe Coelho; Magnavita, Guilherme; Mota, Natália Bezerra; Ziebold, Carolina; Mabunda, Dirceu; Pan, Pedro Mário; Zugman, André; Gadelha, Ary; Corcoran, Cheryl; Bressan, Rodrigo

doi:10.1590/1516-4446-2019-0722

Cited by 10 publications

(6 citation statements)

References 65 publications

(116 reference statements)

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“…In their cluster randomized controlled trial, Gureje et al observed that this approach was effective and cost-effective, circumventing the lack of mental health services and professionals and harmonizing scientific-based intervention with local cultural practices and beliefs. Also, alternative and cost-sparing ways of screening for psychosis should also be employed, given the shortages of resources ( 53 ). Another cost-sparing approach that would be suited for LAMIC concerns the use of speech-based techniques to screen CHR individuals.…”

Section: Discussionmentioning

confidence: 99%

“…Another cost-sparing approach that would be suited for LAMIC concerns the use of speech-based techniques to screen CHR individuals. This has been described in a work by Argolo et al ( 53 ), in which a simulation of a perfect screening software based on language analysis would spare USD 9.34 billion for the healthcare system of Maputo, Mozambique.…”

Section: Discussionmentioning

confidence: 99%

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Inequality and barriers in psychosis prevention: A systematic review on clinical high-risk for psychosis studies from developing countries

et al. 2023

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BackgroundThe clinical high-risk for psychosis (CHR) paradigm is one of the best studied preventive paradigms in psychiatry. However, most studies have been conducted in high-income countries. It is unclear if knowledge from such countries applies to low and middle-income countries (LAMIC), and if there are specific limitations hindering CHR research there. Our aim is to systematically review studies on CHR from LAMIC.MethodsA multistep PRISMA-compliant literature search was performed in PubMed and Web of Science for articles published until 1/03/2022, conducted in LAMIC, addressing the concept and correlates of CHR. Study characteristics as well as limitations were reported. Corresponding authors of the included studies were invited to answer an online poll. Quality assessment was done with the MMAT.ResultsA total of 109 studies were included in the review: none from low-income countries, 8 from lower middle-income countries, and 101 from upper middle-income countries. The most frequent limitations were small sample size (47.9%), cross-sectional design (27.1%), and follow-up issues (20.8%). Mean quality of included studies was of 4.4. Out of the 43 corresponding authors, 12 (27.9%) completed the online poll. They cited further limitations as few financial resources (66.7%), no involvement of population (58.2%) and cultural barriers (41.7%). Seventy five percent researchers reported that CHR research should be conducted differently in LAMIC compared to high-income countries, due to structural and cultural issues. Stigma was mentioned in three out of five sections of the poll.DiscussionResults show the discrepancy of available evidence on CHR in LAMIC, given the shortage of resources in such countries. Future directions should aim to increase the knowledge on individuals at CHR in such settings, and to address stigma and cultural factors that may play a role in the pathways toward care in psychosis.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316816, CRD42022316816.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Inequality and barriers in psychosis prevention: A systematic review on clinical high-risk for psychosis studies from developing countries

et al. 2023

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“…Independent systematic reviews (Argolo et al, 2020;de Boer et al, 2018) suggested that NLP methods are mounting to consistent results in terms of validity and accuracy. However, empirical data mostly relies on limited sample sizes and restricted study designs.…”

Section: Nlp and Arms Screeningmentioning

confidence: 99%

Burnishing the blueprint of speech assessment with natural language processing: methods to characterize subtle impairments on individuals in at-risk mental states from a large urban population.

Argôlo¹,

Ramos²,

Mota³

et al. 2023

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Spoken language is a key source of information for thought disorder evaluation. In the last decades, researchers linked psychopathology phenomena to their counterparts in natural language processing (NLP) analysis. Nonetheless, seemingly opposite traits remain unconciliated. For instance, psychotic speech comprises incoherent trails, but also highly associated ones. In order to address some of the remaining gaps, we leveraged procedures from dynamical systems and graph theory. We examined transcribed interviews of 133 individuals — 60 in at-risk mental states (ARMS) and 73 healthy controls — screened from 4,500 quota-sampled citizens in a large metropolis. SIPS was used to assess psychotic symptoms. NLP features were correlated with psychotic traits (Spearman’s ρ) and ARMS status (Wilcoxon signed-rank tests, general linear models and ensemble machine learning algorithms). The general trait (ω), negative, disorganized and general symptoms were correlated with snippets made of consecutive similar words. Namely, their frequency, average/maximum size, heterogeneity and the average number of unrelated words between such snippets. Positive symptoms were associated with adjective use. Average graph centrality was inversely correlated with the general trait. NLP features presented good performance as input in machine learning classification using the AdaBoost model with Random Forests as base learner (F1 score: 0.83, AUC: 0.93, Balanced Accuracy: 0.86).The existence of loosely connected words (e.g. incoherence, looseness, derailment) is well studied. Conversely, NLP models of perseveration (e.g. higher likelihood of chaining together islands of closely related words) and circumstantiality are brought forth in this work. Evidence shows good performance of NLP for clinical decision support in ARMS screening and assessment of subclinical psychosis. We show that a blueprint for speech-based psychometric evaluation is only a few pieces away. We highlight these fields for future research: clanging (a low hanging fruit), environmental context, task-related differences and interpersonal interactions.

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“…The verbal speech parameters were LIWC, diction, Latent Dirichlet Allocation, and Doc2vec features. The non-verbal speech parameters were composed of conversational, OpenSmile, and DisVoice elements, thus distinguishing diagnosed and undiagnosed subjects with an accuracy of 76.2% [ 52 , 53 ].…”

Section: Speech Featuresmentioning

confidence: 99%

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

et al. 2023

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Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC’s), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

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Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

Cited by 10 publications

References 65 publications

Inequality and barriers in psychosis prevention: A systematic review on clinical high-risk for psychosis studies from developing countries

Inequality and barriers in psychosis prevention: A systematic review on clinical high-risk for psychosis studies from developing countries

Burnishing the blueprint of speech assessment with natural language processing: methods to characterize subtle impairments on individuals in at-risk mental states from a large urban population.

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges

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