Identifying Medications Underlying Communication Atypicalities in Psychotic and Affective Disorders: A Pharmacovigilance Study Within the FDA Adverse Event Reporting System

Fusaroli, Michele; Simonsen, Arndis; Borrie, Stephanie A.; Low, Daniel M.; Parola, Alberto; Raschi, Emanuel; Poluzzi, Elisabetta; Fusaroli, Riccardo

doi:10.1044/2023_jslhr-22-00739

Cited by 2 publications

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

Woensdregt,

Fusaroli,

Rich

et al. 2024

Comput Brain Behav

View full text Add to dashboard Cite

In many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.

show abstract

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

Woensdregt,

Fusaroli,

Rich

et al. 2024

Comput Brain Behav

View full text Add to dashboard Cite

show abstract

Vocal markers of schizophrenia: assessing the generalizability of machine learning models and their clinical applicability

Parola,

Trenckner Jessen,

Rybner

et al. 2024

Preprint

View full text Add to dashboard Cite

Background and Hypothesis: Machine Learning (ML) models have been argued to reliably predict diagnosis and symptoms of schizophrenia based on voice data only. However, it is unclear to what extent such ML markers would generalize to different clinical samples and different languages, a crucial assessment to move towards clinical applicability. In this study, we systematically assessed the generalizability of ML models of vocal markers of schizophrenia across contexts and languages. Study Design: We trained models relying on a large cross-linguistic dataset (Danish, German, Chinese) of 217 patients with schizophrenia and 221 controls, and used a conservative pipeline to minimize overfitting. We tested the models' generalizability on: (i) new participants, speaking the same language; (ii) new participants, speaking a different language; (iii) further, we assessed whether training on data with multiple languages would improve generalizability using Mixture of Expert (MoE) and multilingual models. Results: Model performance was comparable to state-of-the-art findings (F1-score ~ 0.75) within the same language; however, models did not generalize well - showing a substantial decrease - when tested on new languages. The performance of MoE and multilingual models was also generally low (F1-score ~ 0.50). Conclusions: Overall, the cross-linguistic generalizability of vocal markers of schizophrenia is limited. We argue that more emphasis should be placed on collecting large open cross-linguistic datasets to systematically test the generalizability of voice-based ML models, and on identifying more precise mechanisms of how the clinical features of schizophrenia are expressed in language and voice, and how different languages vary in that expression.

show abstract

Identifying Medications Underlying Communication Atypicalities in Psychotic and Affective Disorders: A Pharmacovigilance Study Within the FDA Adverse Event Reporting System

Cited by 2 publications

References 85 publications

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

Vocal markers of schizophrenia: assessing the generalizability of machine learning models and their clinical applicability

Contact Info

Product

Resources

About