Npj mental health research最新文献

Decoding substance use disorder severity from clinical notes using a large language model.

Npj mental health research Pub Date : 2025-02-07 DOI: 10.1038/s44184-024-00114-6

Maria Mahbub, Gregory M Dams, Sudarshan Srinivasan, Caitlin Rizy, Ioana Danciu, Jodie Trafton, Kathryn Knight

{"title":"Decoding substance use disorder severity from clinical notes using a large language model.","authors":"Maria Mahbub, Gregory M Dams, Sudarshan Srinivasan, Caitlin Rizy, Ioana Danciu, Jodie Trafton, Kathryn Knight","doi":"10.1038/s44184-024-00114-6","DOIUrl":"10.1038/s44184-024-00114-6","url":null,"abstract":"Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":"4 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A qualitative analysis of the psychedelic mushroom come-up and come-down.

Npj mental health research Pub Date : 2025-02-07 DOI: 10.1038/s44184-024-00095-6

Ari Brouwer, Joshua K Brown, Earth Erowid, Fire Erowid, Sylvia Thyssen, Charles L Raison, Robin L Carhart-Harris

{"title":"A qualitative analysis of the psychedelic mushroom come-up and come-down.","authors":"Ari Brouwer, Joshua K Brown, Earth Erowid, Fire Erowid, Sylvia Thyssen, Charles L Raison, Robin L Carhart-Harris","doi":"10.1038/s44184-024-00095-6","DOIUrl":"10.1038/s44184-024-00095-6","url":null,"abstract":"Psychedelic therapy has the potential to become a revolutionary and transdiagnostic mental health treatment, yielding enduring benefits that are often attributed to the experiences that coincide with peak psychedelic effects. However, there may be an underrecognized temporal structure to this process that helps explain why psychedelic and related altered states of consciousness can have an initially distressing but ultimately distress-resolving effect. Here we present a qualitative analysis of the self-reported 'come-up' or onset phase, and 'come-down' or falling phase, of the psychedelic experience. Focusing on psilocybin or psilocybin-containing mushroom experience reports submitted to Erowid.org, we use phenomenological, thematic content and word frequency analysis to show that the come-up is more often characterized by negatively valenced feeling states that resemble an acute stress reaction, while the come-down phase is more often characterized by positively valenced feeling states of the sort often observed following recovery from illness or resolution of stress. The therapeutic and theoretical relevance of these findings are discussed.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":"4 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143366965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of individual patient outcomes to psychotherapy vs medication for major depression.

Npj mental health research Pub Date : 2025-02-05 DOI: 10.1038/s44184-025-00119-9

Devon LoParo, Boadie W Dunlop, Charles B Nemeroff, Helen S Mayberg, W Edward Craighead

{"title":"Prediction of individual patient outcomes to psychotherapy vs medication for major depression.","authors":"Devon LoParo, Boadie W Dunlop, Charles B Nemeroff, Helen S Mayberg, W Edward Craighead","doi":"10.1038/s44184-025-00119-9","DOIUrl":"10.1038/s44184-025-00119-9","url":null,"abstract":"Treatments for major depressive disorder (MDD) include antidepressant medications and evidence-based psychotherapies, which are approximately equally efficacious. Individual response to treatment, however, is variable, implying individual differences that could allow for prospective differential prediction of treatment response and personalized treatment recommendation. We used machine learning to develop predictor variables that combined demographic and clinical items from a randomized clinical trial. The variables predicted a meaningful proportion of variance in end-of-treatment depression severity for cognitive behavioral therapy (39.7%), escitalopram (32.1%), and duloxetine (67.7%), leading to a high accuracy in predicting remission (71%). Further, we used these variables to simulate treatment recommendation and found that patients who received their recommended treatment had significantly improved depression severity and remission likelihood. Finally, the prediction algorithms and treatment recommendation tool were externally validated in an independent sample. These results represent a highly promising, easily implemented, potential advance for personalized medicine in MDD treatment.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":"4 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11799290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Author Correction: Sex differences in the association between repetitive negative thinking and neurofilament light. 作者更正：重复性消极思维和神经丝光之间的关联的性别差异。

Npj mental health research Pub Date : 2025-01-18 DOI: 10.1038/s44184-025-00116-y

Yolanda Lau, Amit Bansal, Cassandre Palix, Harriet Demnitz-King, Miranka Wirth, Olga Klimecki, Gael Chetelat, Géraldine Poisnel, Natalie L Marchant

引用次数: 0

Meta analysis of resting frontal alpha asymmetry as a biomarker of depression. 静息额叶α不对称作为抑郁症生物标志物的Meta分析。

Npj mental health research Pub Date : 2025-01-17 DOI: 10.1038/s44184-025-00117-x

Yiwen Luo, Mingcong Tang, Xiwang Fan

引用次数: 0

Decreased impulsiveness and MEG normalization after AI-digital therapy in ADHD children: a RCT. ADHD儿童ai数字化治疗后冲动性降低和MEG正常化：一项随机对照试验。

Npj mental health research Pub Date : 2025-01-09 DOI: 10.1038/s44184-024-00111-9

Danylyna Shpakivska Bilan, Irene Alice Chicchi Giglioli, Pablo Cuesta, Elena Cañadas, Ignacio de Ramón, Fernando Maestú, Jose Alda, Josep Antoni Ramos-Quiroga, Jorge A Herrera, Alfonso Amado, Javier Quintero

{"title":"Decreased impulsiveness and MEG normalization after AI-digital therapy in ADHD children: a RCT.","authors":"Danylyna Shpakivska Bilan, Irene Alice Chicchi Giglioli, Pablo Cuesta, Elena Cañadas, Ignacio de Ramón, Fernando Maestú, Jose Alda, Josep Antoni Ramos-Quiroga, Jorge A Herrera, Alfonso Amado, Javier Quintero","doi":"10.1038/s44184-024-00111-9","DOIUrl":"10.1038/s44184-024-00111-9","url":null,"abstract":"Attention-deficit/hyperactivity disorder (ADHD) presents with symptoms like impulsiveness, inattention, and hyperactivity, often affecting children's academic and social functioning. Non-pharmacological interventions, such as digital cognitive therapy, are emerging as complementary treatments for ADHD. The randomized controlled trial explored the impact of an AI-driven digital cognitive program on impulsiveness, inattentiveness, and neurophysiological markers in 41 children aged 8-12 with ADHD. Participants received either 12 weeks of AI-driven therapy or a placebo intervention. Assessments were conducted pre- and post-intervention and magnetoencephalography (MEG) analyzed brain activity. Results showed significant reductions in impulsiveness and inattentiveness scores in the treatment group, associated with normalized MEG spectral profiles, indicating neuromaturation. Notably, improvements in inhibitory control correlated with spectral profile normalization in the parieto-temporal cortex. Improvements in inhibitory control, linked to normalized spectral profiles, suggest AI-driven digital cognitive therapy can reduce impulsiveness in ADHD children by enhancing neurophysiological efficiency. This emphasizes personalized, technology-driven ADHD treatment, using neurophysiological markers for assessing efficacy.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":"4 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Author Correction: Development of the psychopathological vulnerability index for screening at-risk youths: a Rasch model approach 作者更正：发展精神病理脆弱性指数筛选有风险的青少年：一个Rasch模型方法

Npj mental health research Pub Date : 2024-12-27 DOI: 10.1038/s44184-024-00115-5

Yujing Liao, Haitao Shen, Wenjie Duan, Shanshan Cui, Chunxiu Zheng, Rong Liu, Yawen Jia

引用次数: 0

Harnessing multimodal approaches for depression detection using large language models and facial expressions 利用多模态方法使用大型语言模型和面部表情进行抑郁检测

Npj mental health research Pub Date : 2024-12-23 DOI: 10.1038/s44184-024-00112-8

Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier

{"title":"Harnessing multimodal approaches for depression detection using large language models and facial expressions","authors":"Misha Sadeghi, Robert Richer, Bernhard Egger, Lena Schindler-Gmelch, Lydia Helene Rupp, Farnaz Rahimi, Matthias Berking, Bjoern M. Eskofier","doi":"10.1038/s44184-024-00112-8","DOIUrl":"10.1038/s44184-024-00112-8","url":null,"abstract":"Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression severity using the E-DAIC dataset. We employ Large Language Models (LLMs) to extract depression-related indicators from interview transcripts, utilizing the Patient Health Questionnaire-8 (PHQ-8) score to train the prediction model. Additionally, facial data extracted from video frames is integrated with textual data to create a multimodal model for depression severity prediction. We evaluate three approaches: text-based features, facial features, and a combination of both. Our findings show the best results are achieved by enhancing text data with speech quality assessment, with a mean absolute error of 2.85 and root mean square error of 4.02. This study underscores the potential of automated depression detection, showing text-only models as robust and effective while paving the way for multimodal analysis.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00112-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142880558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of trauma and disaster mental health awareness training in Puerto Rico 在波多黎各开展创伤和灾害心理健康意识培训。

Npj mental health research Pub Date : 2024-12-21 DOI: 10.1038/s44184-024-00110-w

Rosaura Orengo-Aguayo, Regan W. Stewart, Tania del Mar Rodríguez-Sanfiorenzo, Karen G. Martínez-González

引用次数: 0

Causal effects of sedentary breaks on affective and cognitive parameters in daily life: a within-person encouragement design 久坐休息对日常生活中情感和认知参数的因果影响：一个内部鼓励设计。

Npj mental health research Pub Date : 2024-12-21 DOI: 10.1038/s44184-024-00113-7

Marco Giurgiu, Irina Timm, Ulrich W. Ebner-Priemer, Florian Schmiedek, Andreas B. Neubauer

{"title":"Causal effects of sedentary breaks on affective and cognitive parameters in daily life: a within-person encouragement design","authors":"Marco Giurgiu, Irina Timm, Ulrich W. Ebner-Priemer, Florian Schmiedek, Andreas B. Neubauer","doi":"10.1038/s44184-024-00113-7","DOIUrl":"10.1038/s44184-024-00113-7","url":null,"abstract":"Understanding the complex relationship between sedentary breaks, affective well-being and cognition in daily life is critical as modern lifestyles are increasingly characterized by sedentary behavior. Consequently, the World Health Organization, with its slogan “every move counts”, emphasizes a central public health goal: reducing daily time spent in sedentary behavior. Previous studies have provided evidence that short sedentary breaks are feasible to integrate into daily life and can improve affective and cognitive parameters. However, observational studies do not allow for causal interpretation. To overcome this limitation, we conducted the first empirical study that integrated the within-person encouragement approach to test the causal effects of short 3-min sedentary breaks on affective and cognitive parameters in daily life. The results suggest that brief sedentary breaks may have a beneficial impact on valence and energetic arousal. Moreover, our methodological approach powerfully demonstrated the possibility of moving towards causal effects in everyday life.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00113-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0