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Background: Valid scalable biomarkers for predicting longitudinal clinical outcomes in psychiatric research are crucial for optimizing intervention and prevention efforts. Here we recorded spontaneous speech from initially abstinent individuals with cocaine use disorder (iCUD) for use in predicting drug use outcomes., Methods: At baseline, 88 iCUD provided 5-minute speech samples describing the positive consequences of quitting drug use and negative consequences of using drugs. Outcomes, including withdrawal, craving, abstinence days, and recent cocaine use, were assessed at three-month intervals up to one year (57 iCUD included in analyses). Predictive modeling compared natural language processing (NLP) techniques, specifically sentence embeddings with established inventories as targets, with models utilizing standard demographic and baseline psychometric variables., Results: At short time intervals, maximal predictive power was obtained with non-NLP models that also incorporated the same drug use measures (as the outcomes) obtained at baseline, potentially reflecting their slow rate of change, which could be estimated by linear functions. However, for longer-term predictions, speech samples alone demonstrated statistically significant results, with Spearman r ≥ 0.46 and 80% accuracy for predicting abstinence. Hence speech samples may capture non-linear dynamics over extended intervals more effectively than traditional measures. These results need to be replicated in larger and independent samples., Conclusions: Compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other subgroups with cocaine addiction, and to additional substance use disorders and related comorbidity., (Copyright © 2025. Published by Elsevier Inc.)

Importance: Valid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts., Objective: To determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD)., Design: A longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up., Participants: Eighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session., Main Outcomes and Measures: Outcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison., Results: Models using the non-speech variables showed the best predictive performance at three(r>0.45, P <2×10 -3 ) and six months follow-up (r>0.37, P <3×10 -2 ). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P =3×10 -2 ), craving (r=0.72, P =5×10 -5 ), days of abstinence (r=0.76, P =1×10 -5 ), and cocaine use in the past 90 days (r=0.61, P =2×10 -3 ), significantly outperforming the other models for abstinence prediction., Conclusions and Relevance: At short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity., Competing Interests: Conflict of Interest Disclosures None reported.

The diagnosis and treatment of psychiatric disorders depends on the analysis of behavior through language by a clinical specialist. This analysis is subjective in nature and could benefit from automated, objective acoustic and linguistic processing methods. This integrated approach would convey a richer representation of patient speech, particularly for expression of emotion. In this work, we explore the potential of acoustic and prosodic metrics to infer clinical variables and predict psychosis, a condition which produces measurable derailment and tangentiality in patient language. To that purpose, we analyzed the recordings of 32 young patients at high risk of developing clinical psychosis. The subjects were evaluated using the Structured Interview for Prodromal Syndromes/Scale of Prodromal Symptoms (SIPS/SOPS) criteria. To analyze the recordings, we examined the variation of different acoustic and prosodic metrics across time. This preliminary analysis shows that these features can infer negative symptom severity ratings (i.e., SIPS-Btotal), obtaining a Pearson correlation of 0.77 for all the subjects after cross-validated evaluation. In addition, these features can predict development of psychosis with high accuracy above 90%, outperforming classification using clinical variables only. This improved predictive power ultimately can help provide early treatment and improve quality of life for those at risk for developing psychosis.

Amyotrophic lateral sclerosis (ALS) is a degenerative disease which causes death of neurons controlling voluntary muscles. It is currently assessed with subjective clinical measurements, but it would benefit from alternative surrogate biomarkers that can better estimate disease progression. This work analyzes speech and fine motor coordination of subjects recruited by the Answer ALS foundation using data from a mobile app. In addition, clinical variables such as speech, writing and total ALSFRS-R scores are also acquired along with forced and slow vital capacity. Cross-sectional and longitudinal analyses were performed using speech and fine motor features. Results show that both types of features are useful to infer clinical variables especially for males (R 2 =0.79 for ALSFRS-R total score), but their initial values are not helpful to predict speech and motor decline. However, we found that longitudinal progression for bulbar and spinal ALS onset are different and they can be identified with high accuracy by the extracted features.