Cross-linguistic benchmarking of NLP metrics for psychosis research.

D Stein, D Bobrovskiy, M Stede, C Montag, S A Just

Abstract read

In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

D SteinUniversität Potsdam, Potsdam, Germany. dina.stein@jetbrains.com.

D BobrovskiyBiozentrum, University of Basel, Basel, Switzerland.

M StedeUniversität Potsdam, Potsdam, Germany.

C MontagDepartment of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany.

S A JustDepartment of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany. sandra.a.just@uit.no.

Funding

Berlin Institute of Health BIH_PRO_279Friedrich-Wingert-Stiftung DMEA 2021-2022Köhler Stiftung S112/10211/16

6 · The paper itself

Abstract

Language is central to psychosis, making it a key focus for both clinical evaluation and research. Many automated linguistic metrics have been developed to analyze speech in psychosis, yet few studies have compared them systematically. In this study, we benchmarked the performance and robustness of 51 natural language processing (NLP) metrics using two datasets: one with German- and one with Russian-speaking participants. Metric performance was defined as the metrics' capacity to reflect symptom severity and distinguish individuals with psychosis from healthy controls. The best-performing metrics tended to be robust across different clinical rating scales and speech elicitation tasks. Overall, verbosity metrics (e.g., word count) outperformed most other metrics across tasks and languages. Thus, verbosity-based metrics should be used as a baseline when evaluating cross-linguistic NLP metrics that are both clinically meaningful and useful for psychosis research.

Identifiers

PMID42547525

DOI10.1038/s41746-026-03053-y

PMCPMC13433947

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.