Detection of suicidality from medical text using privacy-preserving large language models DOI
Isabella C. Wiest, Falk Gerrik Verhees, Dyke Ferber

et al.

The British Journal of Psychiatry, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: Nov. 5, 2024

Background Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments improve models. This study focuses on using large language models (LLMs) detect suicide risk medical text care. Aims To extract about suicidality status admission notes electronic health records (EHRs) privacy-sensitive, locally hosted LLMs, specifically evaluating efficacy Llama-2 Method We compared performance several variants open source LLM extracting 100 reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies. Results A German fine-tuned model showed highest accuracy (87.5%), sensitivity (83.0%) (91.8%) identifying suicidality, with significant improvements various prompt designs. Conclusions The demonstrates capability particularly Llama-2, accurately while preserving data privacy. suggests their application surveillance systems for emergencies improving management systematic quality control research.

Language: Английский

A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder DOI
Nils R. Winter,

Julian Blanke,

Ramona Leenings

et al.

JAMA Psychiatry, Journal Year: 2024, Volume and Issue: 81(4), P. 386 - 386

Published: Jan. 10, 2024

Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one the most prevalent and disabling disorders, major depressive disorder (MDD), no informative biomarkers have been identified.

Language: Английский

Citations

44

Connectome‐Based Predictive Modeling of Trait Mindfulness DOI Creative Commons
Isaac N. Treves, Aaron Kucyi, Madelynn Park

et al.

Human Brain Mapping, Journal Year: 2025, Volume and Issue: 46(1)

Published: Jan. 1, 2025

ABSTRACT Trait mindfulness refers to one's disposition or tendency pay attention their experiences in the present moment, a non‐judgmental and accepting way. has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting‐state fMRI studies have trait within‐ between‐network connectivity of default‐mode (DMN), fronto‐parietal (FPN), salience networks. However, it is unclear how generalizable findings are, they relate different components mindfulness, other networks brain areas may be involved. To address these gaps, we conducted largest study to‐date, consisting pre‐registered connectome‐based predictive modeling analysis 367 meditation‐naïve adults across three samples collected at sites. In model‐training dataset, did not find connections that predicted overall identified models two subscales, Acting Awareness Non‐judging . Models included both (sets pairwise positively increasing connectivity) negative networks, which showed inverse relationship. The network distinct representations involving FPN DMN, respectively. models, overlapped significantly involved whole prominent involvement somatomotor, visual DMN Only generalized predict subscale scores out‐of‐sample, test datasets. Predictions from were also negatively correlated predictions well‐established mind‐wandering connectome model. We preliminary evidence for based on specific affective cognitive facets. incomplete generalization all sites scanners, limited stability as well substantial overlap between underscores difficulty finding robust markers

Language: Английский

Citations

2

Quality over quantity: powering neuroimaging samples in psychiatry DOI
Carolina Makowski, Thomas E. Nichols, Anders M. Dale

et al.

Neuropsychopharmacology, Journal Year: 2024, Volume and Issue: 50(1), P. 58 - 66

Published: June 20, 2024

Language: Английский

Citations

9

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care DOI Creative Commons
Masaru Tanaka

Biomedicines, Journal Year: 2025, Volume and Issue: 13(1), P. 167 - 167

Published: Jan. 12, 2025

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence mental health disorders like depression schizophrenia, which necessitate precise, innovative approaches. Emerging technologies artificial intelligence, induced pluripotent stem cells, multi-omics potential transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies animal models single-variable analyses continue be used, frequently failing capture complexities human conditions. Summary: This review critically evaluates transition serendipity precision-based in research. It focuses key innovations dynamic systems modeling network-based approaches that use genetic, molecular, environmental data identify new therapeutic targets. Furthermore, it emphasizes importance interdisciplinary collaboration human-specific overcoming limitations Conclusions: We highlight precision psychiatry’s transformative revolutionizing care. paradigm shift, combines cutting-edge systematic frameworks, promises increased diagnostic accuracy, reproducibility, efficiency, paving way tailored better patient outcomes

Language: Английский

Citations

1

Announcing “Technology and Psychiatry” and Expanding Evidence-Based Comments and Reviews in JAMA Psychiatry DOI
Döst Öngür, Roy H. Perlis

JAMA Psychiatry, Journal Year: 2025, Volume and Issue: 82(3), P. 215 - 215

Published: Jan. 22, 2025

Our website uses cookies to enhance your experience. By continuing use our site, or clicking "Continue," you are agreeing Cookie Policy | Continue JAMA Psychiatry HomeNew OnlineCurrent IssueFor Authors Podcast JAMA+ AI Journals Network Open Cardiology Dermatology Health Forum Internal Medicine Neurology Oncology Ophthalmology Otolaryngology–Head & Neck Surgery Pediatrics Archives of (1919-1959) JN Learning / CMESubscribeJobsInstitutions LibrariansReprints Permissions Terms Use Privacy Accessibility Statement 2025 American Medical Association. All Rights Reserved Search Archive Input Term Sign In Individual inCreate an Account Access through institution Purchase Options: Buy this article Rent Subscribe the journal

Language: Английский

Citations

1

Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset DOI Creative Commons
Pablo Ormeño-Arriagada, Gastón Márquez, David Araya

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1132 - 1132

Published: Jan. 23, 2025

Reliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges machine learning models, leading to bias poor generalization. The dataset obtained from the EPIVIGILA system Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers class imbalance. To address this issue, we applied various algorithms, both with without sampling methods, compared them using different classification diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, odds ratio. Our results showed that applying methods improved metric values contributed models better Effectively managing crucial reliable diagnosis. This study enhances understanding of how techniques can improve reliability contribute patient outcomes.

Language: Английский

Citations

1

Interoception, network physiology and the emergence of bodily self-awareness DOI Creative Commons
Diego Candia‐Rivera, Tahnée Engelen, Mariana Babo-Rebelo

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 165, P. 105864 - 105864

Published: Aug. 30, 2024

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français étrangers, laboratoires publics privés.

Citations

7

Identifying major depressive disorder based on cerebral blood flow and brain structure: An explainable multimodal learning study DOI
Jinlong Hu,

Yupeng Hou,

Bo Peng

et al.

Journal of Psychiatric Research, Journal Year: 2025, Volume and Issue: 182, P. 304 - 311

Published: Jan. 4, 2025

Language: Английский

Citations

0

Machine learning-based prediction of illness course in major depression: The relevance of risk factors DOI Creative Commons
Lea Teutenberg, Frederike Stein, Florian Thomas‐Odenthal

et al.

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0

Accurate identification of anxiety and depression based on the dlPFC in an emotional autobiographical memory task: A machine learning approach DOI
Guixiang Wang, Yusen Huang, Yan Zhang

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107503 - 107503

Published: Jan. 18, 2025

Language: Английский

Citations

0