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Desrivières S, Zhang Z, Robinson L, Whelan R, Jollans L, Wang Z, Nees F, Chu C, Bobou M, Du D, Cristea I, Banaschewski T, Barker G, Bokde A, Grigis A, Garavan H, Heinz A, Bruhl R, Martinot JL, Martinot MLP, Artiges E, Orfanos DP, Poustka L, Hohmann S, Millenet S, Fröhner J, Smolka M, Vaidya N, Walter H, Winterer J, Broulidakis M, van Noort B, Stringaris A, Penttilä J, Grimmer Y, Insensee C, Becker A, Zhang Y, King S, Sinclair J, Schumann G, Schmidt U. Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder. RESEARCH SQUARE 2024:rs.3.rs-3777784. [PMID: 38352452 PMCID: PMC10862965 DOI: 10.21203/rs.3.rs-3777784/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.
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Ma YR, Liu Z, Ma D, Zhai P, Guo K, Zhang D, Ji Q. A news-based climate policy uncertainty index for China. Sci Data 2023; 10:881. [PMID: 38065994 PMCID: PMC10709629 DOI: 10.1038/s41597-023-02817-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Climate policies can have a significant impact on the economy. However, these policies have often been associated with uncertainty. Quantitative assessment of the socioeconomic impact of climate policy uncertainty is equally or perhaps more important than looking at the policies themselves. Using a deep learning algorithm-the MacBERT model-this study constructed indices of Chinese climate policy uncertainty (CCPU) at the national, provincial and city levels for the first time. The CCPU indices are based on the text mining of news published by a set of major newspapers in China. A clear upward trend was found in the indices, demonstrating increasing policy uncertainties in China in addressing climate change. There is also evidence of clear regional heterogeneity in subnational indices. The CCPU dataset can provide a useful source of information for government actors, academics and investors in understanding the dynamics of climate policies in China. These indices can also be used to investigate the empirical relationship between climate policy uncertainty and other socioeconomic factors in China.
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Affiliation(s)
- Yan-Ran Ma
- Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
- School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China
| | - Zhenhua Liu
- School of Economics and Management, China University of Mining and Technology, Xuzhou, China
| | - Dandan Ma
- Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China
- School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China
| | - Pengxiang Zhai
- School of Economics and Management, Beihang University, Beijing, China
| | - Kun Guo
- School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
| | - Dayong Zhang
- Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu, China.
| | - Qiang Ji
- Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China.
- School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China.
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Li TMH, Chen J, Law FOC, Li CT, Chan NY, Chan JWY, Chau SWH, Liu Y, Li SX, Zhang J, Leung KS, Wing YK. Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study. JMIR Med Inform 2023; 11:e50221. [PMID: 38054498 DOI: 10.2196/50221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 12/07/2023] Open
Abstract
Background Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature. Objective This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML). Methods This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC). Results Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001). Conclusions This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.
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Affiliation(s)
- Tim M H Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Jie Chen
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Framenia O C Law
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Chun-Tung Li
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Ngan Yin Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Joey W Y Chan
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Steven W H Chau
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Yaping Liu
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Shirley Xin Li
- Department of Psychology, The University of Hong Kong, Hong Kong, China (Hong Kong)
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China (Hong Kong)
| | - Jihui Zhang
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
- Guangdong Mental Health Center, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Guangdong, China
| | - Kwong-Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
- Department of Applied Data Science, Hong Kong Shue Yan University, Hong Kong, China (Hong Kong)
| | - Yun-Kwok Wing
- Li Chiu Kong Family Sleep Assessment Unit, Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong)
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Ryu J, Heisig S, McLaughlin C, Katz M, Mayberg HS, Gu X. A natural language processing approach reveals first-person pronoun usage and non-fluency as markers of therapeutic alliance in psychotherapy. iScience 2023; 26:106860. [PMID: 37255661 PMCID: PMC10225921 DOI: 10.1016/j.isci.2023.106860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/18/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023] Open
Abstract
It remains elusive what language markers derived from psychotherapy sessions are indicative of therapeutic alliance, limiting our capacity to assess and provide feedback on the trusting quality of the patient-clinician relationship. To address this critical knowledge gap, we leveraged feature extraction methods from natural language processing (NLP), a subfield of artificial intelligence, to quantify pronoun and non-fluency language markers that are relevant for communicative and emotional aspects of therapeutic relationships. From twenty-eight transcripts of non-manualized psychotherapy sessions recorded in outpatient clinics, we identified therapists' first-person pronoun usage frequency and patients' speech transition marking relaxed interaction style as potential metrics of alliance. Behavioral data from patients who played an economic game that measures social exchange (i.e. trust game) suggested that therapists' first-person pronoun usage may influence alliance ratings through their diminished trusting behavior toward therapists. Together, this work supports that communicative language features in patient-therapist dialogues could be markers of alliance.
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Affiliation(s)
- Jihan Ryu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen Heisig
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Caroline McLaughlin
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Katz
- Clinical Psychology Doctoral Program, School of Health Professions and Nursing, Long Island University - CW Post Campus, Greenvale, NY, USA
| | - Helen S. Mayberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaosi Gu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Wise T, Robinson OJ, Gillan CM. Identifying Transdiagnostic Mechanisms in Mental Health Using Computational Factor Modeling. Biol Psychiatry 2023; 93:690-703. [PMID: 36725393 PMCID: PMC10017264 DOI: 10.1016/j.biopsych.2022.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/09/2022] [Accepted: 09/27/2022] [Indexed: 02/03/2023]
Abstract
Most psychiatric disorders do not occur in isolation, and most psychiatric symptom dimensions are not uniquely expressed within a single diagnostic category. Current treatments fail to work for around 25% to 40% of individuals, perhaps due at least in part to an overreliance on diagnostic categories in treatment development and allocation. In this review, we describe ongoing efforts in the field to surmount these challenges and precisely characterize psychiatric symptom dimensions using large-scale studies of unselected samples via remote, online, and "citizen science" efforts that take a dimensional, mechanistic approach. We discuss the importance that efforts to identify meaningful psychiatric dimensions be coupled with careful computational modeling to formally specify, test, and potentially falsify candidate mechanisms that underlie transdiagnostic symptom dimensions. We refer to this approach, i.e., where symptom dimensions are identified and validated against computationally well-defined neurocognitive processes, as computational factor modeling. We describe in detail some recent applications of this method to understand transdiagnostic cognitive processes that include model-based planning, metacognition, appetitive processing, and uncertainty estimation. In this context, we highlight how computational factor modeling has been used to identify specific associations between cognition and symptom dimensions and reveal previously obscured relationships, how findings generalize to smaller in-person clinical and nonclinical samples, and how the method is being adapted and optimized beyond its original instantiation. Crucially, we discuss next steps for this area of research, highlighting the value of more direct investigations of treatment response that bridge the gap between basic research and the clinic.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Oliver J Robinson
- Neuroscience and Mental Health Group, Institute of Cognitive Neuroscience, University College London, London, United Kingdom; Research Department of Clinical Education and Health Psychology, University College London, London, United Kingdom
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland.
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Abu-Taieh EM, AlHadid I, Masa’deh R, Alkhawaldeh RS, Khwaldeh S, Alrowwad A. Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192113764. [PMID: 36360644 PMCID: PMC9656283 DOI: 10.3390/ijerph192113764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 05/12/2023]
Abstract
Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents' depression and anxiety constructs as mediators that may account for children's depression and anxiety. The purpose of this paper is to test the influence of different factors on children's depression and anxiety, extending from parents' anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent's anxiety and depression which correlate to their offspring's anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent-child anxiety and depression due to the use of social networks.
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Affiliation(s)
- Evon M. Abu-Taieh
- Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Issam AlHadid
- Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Ra’ed Masa’deh
- Department of Management Information Systems, School of Business, The University of Jordan, Amman 77110, Jordan
| | - Rami S. Alkhawaldeh
- Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Sufian Khwaldeh
- Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
- Department Information Technology, Faculty of Information Technology and Systems, University of Fujairah, Fujairah P.O. Box 2202, United Arab Emirates
| | - Ala’aldin Alrowwad
- Department of Business Management, School of Business, The University of Jordan, Aqaba 77110, Jordan
- Correspondence:
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Nour MM, Liu Y, Dolan RJ. Functional neuroimaging in psychiatry and the case for failing better. Neuron 2022; 110:2524-2544. [PMID: 35981525 DOI: 10.1016/j.neuron.2022.07.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/06/2022] [Accepted: 07/08/2022] [Indexed: 12/27/2022]
Abstract
Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.
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Affiliation(s)
- Matthew M Nour
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
| | - Yunzhe Liu
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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