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Kretzer S, Lawrence AJ, Pollard R, Ma X, Chen PJ, Amasi-Hartoonian N, Pariante C, Vallée C, Meaney M, Dazzan P. The Dynamic Interplay Between Puberty and Structural Brain Development as a Predictor of Mental Health Difficulties in Adolescence: A Systematic Review. Biol Psychiatry 2024; 96:585-603. [PMID: 38925264 DOI: 10.1016/j.biopsych.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 06/16/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Puberty is a time of intense reorganization of brain structure and a high-risk period for the onset of mental health problems, with variations in pubertal timing and tempo intensifying this risk. We conducted 2 systematic reviews of articles published up to February 1, 2024, focusing on 1) the role of brain structure in the relationship between puberty and mental health, and 2) precision psychiatry research evaluating the utility of puberty in making individualized predictions of mental health outcomes in young people. The first review provides inconsistent evidence about whether and how pubertal and psychopathological processes may interact in relation to brain development. While most studies found an association between early puberty and mental health difficulties in adolescents, evidence on whether brain structure mediates this relationship is mixed. The pituitary gland was found to be associated with mental health status during this time, possibly through its central role in regulating puberty and its function in the hypothalamic-pituitary-gonadal and hypothalamic-pituitary-adrenal axes. In the second review, the design of studies that have explored puberty in predictive models did not allow for a quantification of its predictive power. However, when puberty was evaluated through physically observable characteristics rather than hormonal measures, it was more commonly identified as a predictor of depression, anxiety, and suicidality in adolescence. Social processes may be more relevant than biological ones to the link between puberty and mental health problems and represent an important target for educational strategies.
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Affiliation(s)
- Svenja Kretzer
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Singapore Institute for Clinical Sciences, Agency for Science, Technology & Research (A∗STAR) Singapore, Republic of Singapore.
| | - Andrew J Lawrence
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Rebecca Pollard
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Xuemei Ma
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Pei Jung Chen
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Nare Amasi-Hartoonian
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom
| | - Carmine Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Corentin Vallée
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom
| | - Michael Meaney
- Singapore Institute for Clinical Sciences, Agency for Science, Technology & Research (A∗STAR) Singapore, Republic of Singapore; Douglas Hospital Research Centre, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
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Song JH, Tomihama RT, Roh D, Cabrera A, Dardik A, Kiang SC. Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients. Ann Vasc Surg 2024; 107:48-54. [PMID: 38582202 DOI: 10.1016/j.avsg.2023.11.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 11/23/2023] [Indexed: 04/08/2024]
Abstract
Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.
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Affiliation(s)
- Jee Hoon Song
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA
| | - Roger T Tomihama
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Daniel Roh
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Andrew Cabrera
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Alan Dardik
- Division of Vascular Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT
| | - Sharon C Kiang
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA; Division of Vascular Surgery, Department of Surgery, VA Loma Linda Healthcare System, Loma Linda, CA.
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Sy KMP, Chow TYJ, Ickovics JR, Ng R. Narratives of pregnancy across 19 Countries: Analysis of a 1.5-billion-word news media database. PLoS One 2024; 19:e0305866. [PMID: 39213429 PMCID: PMC11364417 DOI: 10.1371/journal.pone.0305866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 06/04/2024] [Indexed: 09/04/2024] Open
Abstract
Pregnancy is a universal experience shaped by sociocultural contexts. News media presents a unique opportunity to analyze public narratives of pregnancy and how it differs across cultures. Our study aims to (1) identify the most prevalent overall themes in news media narratives of pregnancy across 19 English-speaking countries, and (2) compare pregnancy narratives across geographic regions. We used the largest English news media corpus that included over 30 million news articles from more than 7000 news websites across 19 countries, and extracted a one-year data subset (2019; 1.5 billion words). Of the primary search terms 'pregnant' and 'pregnancy', we collated 240,464 descriptors that met criteria of lexical proximity and semantic bonding. Thereafter, we used topic modelling to identify the five most prevalent pregnancy-related themes: (1) complications and risk, (2) crime, (3) celebration, (4) celebrity births, and (5) contraception. Although there were regional differences, themes of complications and risk were most common, comprising 39.6% of all pregnancy narratives in our big-data corpus. The second-most dominant theme was crime (20.8%). Narratives of contraception were more prevalent in Europe, North America, and Oceania (27.2-31.3%) compared to Africa and Asia (11.9-19.6%). Though the vast majority of pregnancies are healthy, themes of complications and risk dominated the news media discourse; unchecked, this may be an avenue for misinformation, stress, and anxiety. In addition, lower prevalence of contraception narratives in Africa and Asia may reflect a gap that requires the attention of policymakers in building culturally-adapted programs to promote family planning and encourage open discussions about sexual health. Results contribute to the academic repository of societal representations of pregnancy through a big-data lens, providing contextual information for future development, implementation and evaluation of localized pregnancy-related campaigns.
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Affiliation(s)
- Kalla Maxine P. Sy
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, United States of America
- Yale-NUS College, Singapore, Singapore
| | - Ting Yu Joanne Chow
- Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore
| | - Jeannette R. Ickovics
- Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, CT, United States of America
- Yale-NUS College, Singapore, Singapore
| | - Reuben Ng
- Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore, Singapore
- Lloyd’s Register Foundation Institute for the Public Understanding of Risk, National University of Singapore, Singapore, Singapore
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Razavi M, Ziyadidegan S, Mahmoudzadeh A, Kazeminasab S, Baharlouei E, Janfaza V, Jahromi R, Sasangohar F. Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review. JMIR Ment Health 2024; 11:e53714. [PMID: 39167782 DOI: 10.2196/53714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 05/01/2024] [Accepted: 05/17/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs. OBJECTIVE This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs. METHODS Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs. RESULTS A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. CONCLUSIONS The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.
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Affiliation(s)
- Moein Razavi
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
| | - Samira Ziyadidegan
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
| | - Ahmadreza Mahmoudzadeh
- Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, United States
| | - Saber Kazeminasab
- Harvard Medical School, Harvard University, Boston, MA, United States
| | - Elaheh Baharlouei
- Department of Computer Science, University of Houston, Houston, TX, United States
| | - Vahid Janfaza
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
| | - Reza Jahromi
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States
| | - Farzan Sasangohar
- Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States
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Laricheva M, Liu Y, Shi E, Wu A. Scoping review on natural language processing applications in counselling and psychotherapy. Br J Psychol 2024. [PMID: 39095975 DOI: 10.1111/bjop.12721] [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: 01/08/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024]
Abstract
Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up-to-date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021. The purpose of this scoping review is to identify trends, advancements, challenges and limitations of these applications. Among the 41 papers included in this review, 4 primary study purposes were identified: (1) developing automated coding; (2) predicting outcomes; (3) monitoring counselling sessions; and (4) investigating language patterns. Our findings showed a growing trend in the number of papers utilizing advanced machine learning methods, particularly neural networks. Unfortunately, only a third of the articles addressed the issues of bias and generalizability. Our findings provided a timely systematic update, shedding light on concerns related to bias, generalizability and validity in the context of NLP applications in counselling and psychotherapy.
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Affiliation(s)
- Maria Laricheva
- Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Yan Liu
- Psychology, Carleton University, Ottawa, Ontario, Canada
| | - Edward Shi
- Arts, Business and Law, Victoria University Melbourne, Melbourne, Victoria, Australia
| | - Amery Wu
- Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, British Columbia, Canada
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Jiang H, Hu R, Wang YJ, Xie X. Predicting depression in patients with heart failure based on a stacking model. World J Clin Cases 2024; 12:4661-4672. [PMID: 39070824 PMCID: PMC11235518 DOI: 10.12998/wjcc.v12.i21.4661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 05/27/2024] [Accepted: 06/17/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure (HF). AIM To create a stacking model for predicting depression in patients with HF. METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018. Through univariate analysis and the use of an artificial neural network algorithm, predictors significantly linked to depression were identified. These predictors were utilized to create a stacking model employing tree-based learners. The performances of both the individual models and the stacking model were assessed by using the test dataset. Furthermore, the SHapley additive exPlanations (SHAP) model was applied to interpret the stacking model. RESULTS The models included five predictors. Among these models, the stacking model demonstrated the highest performance, achieving an area under the curve of 0.77 (95%CI: 0.71-0.84), a sensitivity of 0.71, and a specificity of 0.68. The calibration curve supported the reliability of the models, and decision curve analysis confirmed their clinical value. The SHAP plot demonstrated that age had the most significant impact on the stacking model's output. CONCLUSION The stacking model demonstrated strong predictive performance. Clinicians can utilize this model to identify high-risk depression patients with HF, thus enabling early provision of psychological interventions.
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Affiliation(s)
- Hui Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Rui Hu
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
| | - Yu-Jie Wang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui Province, China
| | - Xiang Xie
- Department of Ultrasound Diagnosis, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China
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Taylor B, Hobensack M, Niño de Rivera S, Zhao Y, Masterson Creber R, Cato K. Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review. JMIR Nurs 2024; 7:e54810. [PMID: 39028994 PMCID: PMC11297379 DOI: 10.2196/54810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. OBJECTIVE This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. METHODS This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. CONCLUSIONS The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.
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Affiliation(s)
- Brittany Taylor
- School of Nursing, Columbia University, New York, NY, United States
| | - Mollie Hobensack
- Brookdale Department of Geriatrics and Palliative Care, Icahn School of Medicine, Mount Sinai Health System, New York, NY, United States
| | | | - Yihong Zhao
- School of Nursing, Columbia University, New York, NY, United States
| | | | - Kenrick Cato
- School of Nursing, University of Pennsylvania, Philadelphia, PA, United States
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Ramanarayanan V. Multimodal Technologies for Remote Assessment of Neurological and Mental Health. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024:1-13. [PMID: 38984943 DOI: 10.1044/2024_jslhr-24-00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
PURPOSE Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. APPROACH We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. FINDINGS We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. CONCLUSION This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.
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Affiliation(s)
- Vikram Ramanarayanan
- Modality.AI, Inc., San Francisco, CA
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco
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Jiang H, He Y, Lan X, Xie X. Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning. Sci Rep 2024; 14:15578. [PMID: 38971817 PMCID: PMC11227570 DOI: 10.1038/s41598-024-66162-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 06/27/2024] [Indexed: 07/08/2024] Open
Abstract
There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.
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Affiliation(s)
- Hui Jiang
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical Universty, Hefei, 230601, Anhui, China
| | - Yanbin He
- Dian Diagnostics Group Co., Ltd, Hangzhou, 310000, Zhejiang, China
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Hangzhou, 310030, Zhejiang, China
| | - Xiaofeng Lan
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical Universty, Hefei, 230601, Anhui, China
| | - Xiang Xie
- Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical Universty, Hefei, 230601, Anhui, China.
- Department of Interventional Ultrasound, The Second Affiliated Hospital of Anhui Medical Universty, Hefei, 230601, Anhui, China.
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Garbazza C, Mangili F, D'Onofrio TA, Malpetti D, Riccardi S, Cicolin A, D'Agostino A, Cirignotta F, Manconi M. A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort. Psychiatry Res 2024; 337:115957. [PMID: 38788556 DOI: 10.1016/j.psychres.2024.115957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024]
Abstract
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this condition. We applied a machine learning (ML) approach to data from a multicenter cohort study on sleep and mood changes during the perinatal period ("Life-ON") to derive models for PND risk prediction in a cross-validation setting. A wide range of sociodemographic variables, blood-based biomarkers, sleep, medical, and psychological data collected from 439 pregnant women, as well as polysomnographic parameters recorded from 353 women, were considered for model building. These covariates were correlated with the risk of future depression, as assessed by regularly administering the Edinburgh Postnatal Depression Scale across the perinatal period. The ML model indicated the mood status of pregnant women in the first trimester, previous depressive episodes and marital status, as the most important predictors of PND. Sleep quality, insomnia symptoms, age, previous miscarriages, and stressful life events also added to the model performance. Besides other predictors, sleep changes during early pregnancy should therefore assessed to identify women at higher risk of PND and support them with appropriate therapeutic strategies.
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Affiliation(s)
- Corrado Garbazza
- Sleep Medicine Unit, Neurocenter of Southern Switzerland, Lugano, Switzerland; Centre for Chronobiology, University of Basel, Basel, Switzerland; Research Cluster Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland.
| | - Francesca Mangili
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Tatiana Adele D'Onofrio
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Daniele Malpetti
- Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), USI/SUPSI, Lugano, Switzerland
| | - Silvia Riccardi
- Sleep Medicine Unit, Neurocenter of Southern Switzerland, Lugano, Switzerland
| | - Alessandro Cicolin
- Sleep Medicine Center, Department of Neuroscience, University of Turin, Turin, Italy
| | - Armando D'Agostino
- Department of Mental Health and Addiction, ASST Santi Paolo e Carlo, Milan, Italy; Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | | | - Mauro Manconi
- Sleep Medicine Unit, Neurocenter of Southern Switzerland, Lugano, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland; Department of Neurology, University Hospital, Inselspital, Bern, Switzerland
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Medenica V, Ivanovic L, Milosevic N. Applicability of artificial intelligence in neuropsychological rehabilitation of patients with brain injury. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-28. [PMID: 38912923 DOI: 10.1080/23279095.2024.2364229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Neuropsychological rehabilitation plays a critical role in helping those recovering from brain injuries restore cognitive and functional abilities. Artificial Intelligence, with its potential, may revolutionize this field further; therefore, this article explores applications of AI for neuropsychological rehabilitation of patients suffering brain injuries. This study employs a systematic review methodology to comprehensively review existing literature regarding Artificial Intelligence use in neuropsychological rehabilitation for people with brain injuries. The systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of electronic databases (PubMed, Scopus, PsycINFO, etc.) showed a total of 212 potentially relevant articles. After removing duplicates and screening titles and abstracts, 186 articles were selected for assessment. Following the assessment, 55 articles met the inclusion criteria and were included in this systematic review. A thematic analysis approach is employed to analyze and synthesize the extracted data. Themes, patterns, and trends are identified across the included studies, allowing for a comprehensive understanding of the applicability of AI in neuropsychological rehabilitation for patients with brain injuries. The identified topics were: AI Applications in Diagnostics of Brain Injuries and their Neuropsychological Repercussions; AI in Personalization and Monitoring of Neuropsychological Rehabilitation for traumatic brain injury (TBI); Leveraging AI for Predicting and Optimizing Neuropsychological Rehabilitation Outcomes in TBI Patients. Based on the review, it was concluded that AI has the potential to enhance neuropsychological rehabilitation for patients with brain injuries. By leveraging AI techniques, personalized rehabilitation programs can be developed, treatment outcomes can be predicted, and interventions can be optimized.
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Affiliation(s)
- Veselin Medenica
- Department of Occupational Therapy, The College of Human Development, Belgrade, Serbia
| | - Lidija Ivanovic
- Department of Occupational Therapy, The College of Human Development, Belgrade, Serbia
| | - Neda Milosevic
- Department of Occupational Therapy, The College of Human Development, Belgrade, Serbia
- Department of Speech Therapy, The College of Human Development, Belgrade, Serbia
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Vistorte AOR, Deroncele-Acosta A, Ayala JLM, Barrasa A, López-Granero C, Martí-González M. Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Front Psychol 2024; 15:1387089. [PMID: 38966729 PMCID: PMC11223560 DOI: 10.3389/fpsyg.2024.1387089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 05/22/2024] [Indexed: 07/06/2024] Open
Abstract
Introduction Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students' emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.
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Affiliation(s)
- Angel Olider Rojas Vistorte
- Psychology Department, European University of the Atlantic, Santander, Spain
- Psychology Department, International Ibero-American University, Mexico, Mexico
| | | | - Juan Luis Martín Ayala
- Psychology Department, European University of the Atlantic, Santander, Spain
- Psychology Department, International Ibero-American University, Mexico, Mexico
| | - Angel Barrasa
- Department of Psychology and Sociology, University of Zaragoza, Teruel, Spain
| | | | - Mariacarla Martí-González
- Department of Social Anthropology and Social Psychology, Complutense University of Madrid, Madrid, Spain
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Sci Rep 2024; 14:13859. [PMID: 38879556 PMCID: PMC11180169 DOI: 10.1038/s41598-024-64487-6] [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: 03/11/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.
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Affiliation(s)
- Inga Meyhoefer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Andreas Sprenger
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - David Derad
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Marian Surmann
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Karen Rolfes
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Georg Romer
- Department of Child Adolescence Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Johanna Rehder
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck-Institute of Psychiatry Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
| | - Elena I Ivleva
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - James L Reilly
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, and Olin Research Center, Institute of Living/Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany.
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany.
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
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Chen X, Chen J, Zhao X, Mu R, Tan H, Yu Z. Issues and Solutions in Psychiatric Clinical Trial with Case Studies. Neuropsychiatr Dis Treat 2024; 20:1191-1200. [PMID: 38855383 PMCID: PMC11162181 DOI: 10.2147/ndt.s454813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/22/2024] [Indexed: 06/11/2024] Open
Abstract
The coronavirus disease-2019 pandemic resulted in a major increase in depression and anxiety disorders worldwide, which increased the demand for mental health services. However, clinical interventions for treating mental disorders are currently insufficient to meet this growing demand. There is an urgent need to conduct scientific and standardized clinical research that are consistent with the features of mental disorders in order to deliver more effective and safer therapies in the clinic. Our study aimed to expose the challenges, complexities of study design, ethical issues, sample selection, and efficacy evaluation in clinical research for mental disorders. The reliance on subjective symptom presentation and rating scales for diagnosing mental diseases was discovered, emphasizing the lack of clear biological standards, which hampers the construction of rigorous research criteria. We underlined the possibility of psychotherapy in efficacy evaluation alongside medication treatment, proposing for a multidisciplinary approach comprising psychiatrists, neuroscientists, and statisticians. To comprehend mental disorders progression, we recommend the development of artificial intelligence integrated evaluation tools, the use of precise biomarkers, and the strengthening of longitudinal designs. In addition, we advocate for international collaboration to diversity samples and increase the dependability of findings, with the goal of improving clinical research quality in mental disorders through sample representativeness, accurate medical history gathering, and adherence to ethical principles.
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Affiliation(s)
- Xiaochen Chen
- Clinical Research Institute & School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Jun Chen
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Xue Zhao
- Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Rongji Mu
- Clinical Research Institute & School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Hongsheng Tan
- Clinical Research Institute & School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Zhangsheng Yu
- Clinical Research Institute & School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
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15
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Norris ML, Obeid N, El-Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. Int J Eat Disord 2024; 57:1357-1368. [PMID: 38597344 DOI: 10.1002/eat.24215] [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: 11/17/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024]
Abstract
OBJECTIVE To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. METHOD An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. RESULTS In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. DISCUSSION There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. PUBLIC SIGNIFICANCE Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.
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Affiliation(s)
- Mark L Norris
- Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO), University of Ottawa, Ottawa, Ontario, Canada
- CHEO Research Institute, Ottawa, Ontario, Canada
| | - Nicole Obeid
- CHEO Research Institute, Ottawa, Ontario, Canada
- Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada
| | - Khaled El-Emam
- CHEO Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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16
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Książek K, Masarczyk W, Głomb P, Romaszewski M, Stokłosa I, Ścisło P, Dębski P, Pudlo R, Buza K, Gorczyca P, Piegza M. Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data. Comput Biol Med 2024; 176:108544. [PMID: 38723395 DOI: 10.1016/j.compbiomed.2024.108544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). METHOD We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. RESULTS Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. CONCLUSIONS HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.
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Affiliation(s)
- Kamil Książek
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland.
| | - Wilhelm Masarczyk
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Przemysław Głomb
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland
| | - Michał Romaszewski
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland
| | - Iga Stokłosa
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Piotr Ścisło
- Psychiatric Department of the Multidisciplinary Hospital, Tarnowskie Góry, 42-612, Poland
| | - Paweł Dębski
- Institute of Psychology, Humanitas University in Sosnowiec, Kilińskiego 43, Sosnowiec, 41-200, Poland
| | - Robert Pudlo
- Department of Psychoprophylaxis, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Krisztián Buza
- Budapest Business University, Buzogány utca 10-12, Budapest, 1149, Hungary; BioIntelligence Group, Department of Mathematics-Informatics, Sapientia Hungarian University of Transylvania, Târgu Mureş, Romania
| | - Piotr Gorczyca
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Magdalena Piegza
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
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17
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Chen G, Liu Y, Mao Y. Understanding the log file data from educational and psychological computer-based testing: A scoping review protocol. PLoS One 2024; 19:e0304109. [PMID: 38781194 PMCID: PMC11115232 DOI: 10.1371/journal.pone.0304109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
With the advancement of computer-based testing, log file data has drawn considerable attention from researchers. Although emerging studies have begun to explore log file data, there is a gap in the exploitation of log file data for capturing and understanding participants' cognitive processes. The debate on how to maximize insights from log file data has not yet reached a consensus. Therefore, we present this protocol for a scoping review that aims to characterize the application of log file data in current publications, including the data pre-processing techniques, analytical methodologies, and theoretical frameworks used by researchers. This review will also aim to illuminate how log file data can enhance psychological and educational assessments. Our findings will highlight the opportunities and challenges presented by log file data as an emerging and essential source of evidence for future advancements in psychological and educational assessment.
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Affiliation(s)
- Guanyu Chen
- Department of Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, Canada
| | - Yan Liu
- Department of Psychology, Carleton University, Ottawa, Canada
| | - Yue Mao
- Department of Educational and Counselling Psychology, and Special Education, The University of British Columbia, Vancouver, Canada
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Atzil-Slonim D, Penedo JMG, Lutz W. Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2024; 51:306-317. [PMID: 37880473 DOI: 10.1007/s10488-023-01309-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/29/2023] [Indexed: 10/27/2023]
Abstract
Mental health services are experiencing notable transformations as innovative technologies and artificial intelligence (AI) are increasingly utilized in a growing number of studies and services.These cutting-edge technologies carry the promise of substantial improvements in the field of mental health. Nevertheless, questions emerge about the alignment of novel technologies and AI systems with human needs, especially in the context of vulnerable populations receiving mental healthcare. The practice-oriented research (POR) model is pivotal in seamlessly integrating these emerging technologies into clinical research and practice. It underscores the importance of tight collaboration between clinicians and researchers, all driven by the central goal of ensuring and elevating client well-being. This paper focuses on how novel technologies can enhance the POR model and highlights its pivotal role in integrating these technologies into clinical research and practice. We discuss two key phases: pre-treatment, and during treatment. For each phase, we describe the challenges, present the major technological innovations, describe recent studies exemplifying technology use, and suggest future directions. Ethical concerns and the importance of aligning humans and technology are also considered, in addition to implications for practice and training.
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Affiliation(s)
| | | | - Wolfgang Lutz
- Department of Psychology, University of Trier, Trier, Germany
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Hong L, Yang A, Liang Q, He Y, Wang Y, Tao S, Chen L. Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China. PREVENTION SCIENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR PREVENTION RESEARCH 2024; 25:699-710. [PMID: 37897552 DOI: 10.1007/s11121-023-01610-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2023] [Indexed: 10/30/2023]
Abstract
Maternal depression (MD) was one of the most prevalent psychiatric problems worldwide. However, it easily remains untreated and misses the best time to prevent the emergence or worsening of major depressive symptoms due to under-observed stigma and the lack of effective screening tools. Thus, this study aims to develop and validate a machine learning-based MD symptoms prediction model integrating more observable and objective factors to early detect and monitor MD risk. A cross-sectional study was conducted in 10 community vaccination centers in Wenzhou, China, and a total of 1099 mothers were surveyed by using purposive sampling. A questionnaire containing questions regarding socio-demographic variables, psychophysiological variables, wife role-related variables, and mother role-related variables was used to collect data. A framework of data preprocessing, feature selection, and model evaluation was implemented to develop an optimal risk prediction model. Results demonstrated that the XG-Boost algorithm provided robust performance with the highest AUC and well-balanced sensitivity and specificity (AUC = 0.90, sensitivity = 0.74, specificity = 0.90). Furthermore, the causal mediation analysis indicated that wife-mother role conflict positively predicted MD symptoms, and it also exerted influence on mothers suffering through the mediation of anxiety and insomnia. Findings from the present study may help guide the development of MD screening tools to early detect and provide the modifiable risk factor information for timely tailored prevention.
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Affiliation(s)
- Liuzhi Hong
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Ai Yang
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Qi Liang
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Yuhan He
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Yulin Wang
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Shuhan Tao
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Li Chen
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China.
- The Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, 325035, China.
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20
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Lin S, Wang C, Jiang X, Zhang Q, Luo D, Li J, Li J, Xu J. Using machine learning to develop a five-item short form of the children's depression inventory. BMC Public Health 2024; 24:1118. [PMID: 38654267 DOI: 10.1186/s12889-024-18657-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/18/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version. With its 27 items requiring professional administration, the original CDI proves to be a time-consuming method for predicting children and adolescents with high depression risk. Hence, this study aimed to develop a shortened version of the CDI to predict high depression risk, thereby enhancing the efficiency of prediction and intervention. METHODS Initially, backward elimination is conducted to identify various version of the short-form scales (e.g., three-item and five-item versions). Subsequently, the performance of five machine learning (ML) algorithms on these versions is evaluated using the area under the ROC curve (AUC) to determine the best algorithm. The chosen algorithm is then utilized to model the short-form scales, facilitating the identification of the optimal short-form scale based on predefined evaluation metrics. Following this, evaluation metrics are computed for all potential decision thresholds of the optimal short-form scale, and the threshold value is determined. Finally, the reliability and validity of the optimal short-form scale are assessed using a new sample. RESULTS The study identified a five-item short-form CDI with a decision threshold of 4 as the most appropriate scale considering all assessment indicators. The scale had 81.48% fewer items than the original version, indicating good predictive performance (AUC = 0.81, Accuracy = 0.83, Recall = 0.76, Precision = 0.71). Based on the test of 315 middle school students, the results showed that the five-item CDI had good measurement indexes (Cronbach's alpha = 0.72, criterion-related validity = 0.77). CONCLUSIONS This five-item short-form CDI is the first shortened and revised version of the CDI in China based on large local data samples.
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Affiliation(s)
- Shumei Lin
- College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China
| | - Chengwei Wang
- Department of Integrated Traditional and Western Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiuyu Jiang
- College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China
| | - Qian Zhang
- College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China
| | - Dan Luo
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Junyi Li
- College of Psychology, Sichuan Normal University, Chengdu, Sichuan, China.
- Sichuan Key Laboratory of Psychology and Behavior of Discipline Inspection and Supervision, Sichuan Normal University, Chengdu, China.
| | - Jiajun Xu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Cherblanc J, Gaboury S, Maître J, Côté I, Cadell S, Bergeron-Leclerc C. Predicting levels of prolonged grief disorder symptoms during the COVID-19 pandemic: An integrated approach of classical data exploration, predictive machine learning, and explainable AI. J Affect Disord 2024; 351:746-754. [PMID: 38290589 DOI: 10.1016/j.jad.2024.01.236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 01/11/2024] [Accepted: 01/26/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND Prior studies on Prolonged Grief Disorder (PGD) primarily employed classical approaches to link bereaved individuals' characteristics with PGD symptom levels. This study utilized machine learning to identify key factors influencing PGD symptoms during the COVID-19 pandemic. METHODS We analyzed data from 479 participants through an online survey, employing classical data exploration, predictive machine learning, and SHapley Additive exPlanations (SHAP) to determine key factors influencing PGD symptoms measured with the Traumatic Grief Inventory - Self Report (TGI-SR) from 19 variables, comparing five predictive models. RESULTS The classical approach identified eight variables associated with a possible PGD (TGI-SR score ≥ 59): unexpected causes of death, living alone, seeking professional support, taking anxiety and/or depression medications, using more grief services (telephone or online supports) and more confrontation-oriented coping strategies, and higher levels of depression and anxiety. Using machine learning techniques, the CatBoost algorithm provided the best predictive model of the TGI-SR score (r2 = 0.6479). The three variables influencing the most the level of PGD symptoms were anxiety, and levels of avoidance and confrontation coping strategies used. CONCLUSIONS This pioneering approach within the field of grief research enabled us to leverage the extensive dataset collected during the pandemic, facilitating a deeper comprehension of the predominant factors influencing the grieving process for individuals who experienced loss during this period. LIMITATIONS This study acknowledges self-selection bias, limited sample diversity, and suggests further research is needed to fully understand the predictors of PGD symptoms.
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Gholi Zadeh Kharrat F, Gagne C, Lesage A, Gariépy G, Pelletier JF, Brousseau-Paradis C, Rochette L, Pelletier E, Lévesque P, Mohammed M, Wang J. Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec. PLoS One 2024; 19:e0301117. [PMID: 38568987 PMCID: PMC10990247 DOI: 10.1371/journal.pone.0301117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 03/11/2024] [Indexed: 04/05/2024] Open
Abstract
Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models' output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.
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Affiliation(s)
- Fatemeh Gholi Zadeh Kharrat
- Institut Intelligence et Données (IID), Université Laval, Québec, Québec, Canada
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Christian Gagne
- Institut Intelligence et Données (IID), Université Laval, Québec, Québec, Canada
| | - Alain Lesage
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Geneviève Gariépy
- Centre for Surveillance and Applied Research, Health Promotion and Chronic Disease Prevention Branch, Public Health Agency of Canada, Ottawa, Canada
- Department of Social and Preventive Medicine, School of Public Health, University of Montreal, Montreal, Canada
- Montreal Mental Health University Institute Research Center, Montreal, Canada
| | - Jean-François Pelletier
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Camille Brousseau-Paradis
- Department of Psychiatry and Addiction, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire en Santé Mentale de Montréal, Québec, Canada
| | - Louis Rochette
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Eric Pelletier
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Pascale Lévesque
- Institut National de Santé Publique du Québec (INSPQ), Québec, Québec, Canada
| | - Mada Mohammed
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
| | - JianLi Wang
- Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, Halifax, Canada
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23
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Bhimavarapu U. Stacked artificial neural network to predict the mental illness during the COVID-19 pandemic. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01799-8. [PMID: 38558146 DOI: 10.1007/s00406-024-01799-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 03/09/2024] [Indexed: 04/04/2024]
Abstract
The individual's mental health crisis and the COVID-19 pandemic lead to mental disorders. The transmission of the COVID-19 virus is associated with the levels of anxiety, stress, and depression in individuals, similar to other factors. Increases in mental illness cases and the prevalence of depression have peaked after the pandemic struck. The limited social intervention, reduced communication, peer support, and increased social isolation during the pandemic resulted in higher levels of depression, stress, and anxiety which leads to mental illness. Physiological distress is associated with the mental disorders, and its negative impact can be improved mainly by early detection and treatment. Early identification of mental illness is crucial for timely intervention to decelerate disorder severity and lessen individual health burdens. Laboratory tests for diagnosing mental illness depend on the self-reports of one's mental status, but it is labor intensive and time consuming. Traditional methods like linear or nonlinear regression cannot include many explanatory variables as they are prone to overfitting. The main challenge of the state-of-the-art models is the poor performance in detecting mental illnesses at early stages. Deep learning models can handle numerous variables. The current study focuses on demographic background, Kessler Psychological Distress, Happiness, and Health determinants of mental health during the pandemic to predict the mental health. This study's prediction can help rapid diagnosis and treatment and promote overall public mental health. Despite potential response bias, these proportions are exceptionally elevated, and it's plausible that certain individuals face an even higher level of risk. In the context of the COVID-19 pandemic, an investigation into mental health patients revealed a disproportionate representation of children and individuals with neurotic disorders among those articulating substantial or severe apprehensions.
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Affiliation(s)
- Usharani Bhimavarapu
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India.
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24
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Luo Y, Deznabi I, Shaw A, Simsiri N, Rahman T, Fiterau M. Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data. Sci Rep 2024; 14:6631. [PMID: 38503794 PMCID: PMC10951234 DOI: 10.1038/s41598-024-56674-2] [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: 10/11/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
College students experience ever-increasing levels of stress, leading to a wide range of health problems. In this context, monitoring and predicting students' stress levels is crucial and, fortunately, made possible by the growing support for data collection via mobile devices. However, predicting stress levels from mobile phone data remains a challenging task, and off-the-shelf deep learning models are inapplicable or inefficient due to data irregularity, inter-subject variability, and the "cold start problem". To overcome these challenges, we developed a platform named Branched CALM-Net that aims to predict students' stress levels through dynamic clustering in a personalized manner. This is the first platform that leverages the branching technique in a multitask setting to achieve personalization and continuous adaptation. Our method achieves state-of-the-art performance in predicting student stress from mobile sensor data collected as part of the Dartmouth StudentLife study, with a ROC AUC 37% higher and a PR AUC surpassing that of the nearest baseline models. In the cold-start online learning setting, Branched CALM-Net outperforms other models, attaining an average F1 score of 87% with just 1 week of training data for a new student, which shows it is reliable and effective at predicting stress levels from mobile data.
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Affiliation(s)
- Yunfei Luo
- Manning College of Information and Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, USA.
- Halıcıoğlu Data Science Institute, University of California San Diego, 9500 Gilman Dr, San Diego, CA, 92093, USA.
| | - Iman Deznabi
- Manning College of Information and Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, USA
| | - Abhinav Shaw
- Manning College of Information and Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, USA
- Computer Science, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA
| | - Natcha Simsiri
- Manning College of Information and Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, USA
| | - Tauhidur Rahman
- Halıcıoğlu Data Science Institute, University of California San Diego, 9500 Gilman Dr, San Diego, CA, 92093, USA
| | - Madalina Fiterau
- Manning College of Information and Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA, 01003, USA
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25
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Xing Y, van Erp TG, Pearlson GD, Kochunov P, Calhoun VD, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. iScience 2024; 27:109319. [PMID: 38482500 PMCID: PMC10933544 DOI: 10.1016/j.isci.2024.109319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/17/2023] [Accepted: 02/19/2024] [Indexed: 04/26/2024] Open
Abstract
The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.
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Affiliation(s)
- Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA 92617, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and of Neurobiology, Yale University, New Haven, CT 06519, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center and Department of Psychiatry, University of Maryland, School of Medicine, Baltimore, MD 21201, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
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26
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Gargano MC, DiBiase CE, Miller-Graff LE. What words can tell us about social determinants of mental health: A multi-method analysis of sentiment towards migration experiences and community life in Lima, Perú. Transcult Psychiatry 2024:13634615231213837. [PMID: 38454760 DOI: 10.1177/13634615231213837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
To support resilience in contexts of migration, a deeper understanding of the experiences of both receiving communities and migrants is required. Research on the impacts of migration on community life is limited in contexts with high internal migration (i.e., migrating within one's country of origin). Evidence suggests that cultural similarity, community relationships, and access to resources may be protective factors that could be leveraged to support the mental health of internal migrants. The current study uses data drawn from a sample of pregnant Peruvian women (N = 251), 87 of whom reported being internal migrants and 164 of whom reported being from the locale of the study (Lima, Perú). The aim was to better understand the social experience of internal migration for both local and migrant women. Inductive thematic analysis was used to examine migration experience and perceived impact of migration on community life. Internal migrants discussed three themes relative to their experiences: motivations, adjustment, and challenges. Experiences of women in receiving communities consisted of four themes related to migration: positive, negative, neutral, and mixed perceptions. Linguistic Inquiry and Word Count (LIWC-22) software was also used to assess sentiment towards migration. Across both analytic methods, migration motivations and perceptions were multifaceted and migrants reported a wide range of challenges before, during, and after migration. Findings indicated that attitudes toward migration are broadly positive, and that there is a more positive appraisal of migration's impact on the community life for internal as opposed to international migration.
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Affiliation(s)
- Maria Caterina Gargano
- Department of Psychology and Kroc Institute for International Peace Studies, University of Notre Dame
| | | | - Laura E Miller-Graff
- Department of Psychology and Kroc Institute for International Peace Studies, University of Notre Dame
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27
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Zafar F, Fakhare Alam L, Vivas RR, Wang J, Whei SJ, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024; 16:e56472. [PMID: 38638735 PMCID: PMC11025697 DOI: 10.7759/cureus.56472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.
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Affiliation(s)
- Fabeha Zafar
- Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK
| | | | - Rafael R Vivas
- Nutrition, Food and Exercise Sciences, Florida State University College of Human Sciences, Tallahassee, USA
| | - Jada Wang
- Medicine, St. George's University, Brooklyn, USA
| | - See Jia Whei
- Internal Medicine, Sriwijaya University, Palembang, IDN
| | | | | | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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28
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Eberhardt ST, Schaffrath J, Moggia D, Schwartz B, Jaehde M, Rubel JA, Baur T, André E, Lutz W. Decoding emotions: Exploring the validity of sentiment analysis in psychotherapy. Psychother Res 2024:1-16. [PMID: 38415369 DOI: 10.1080/10503307.2024.2322522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 02/15/2024] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVE Given the importance of emotions in psychotherapy, valid measures are essential for research and practice. As emotions are expressed at different levels, multimodal measurements are needed for a nuanced assessment. Natural Language Processing (NLP) could augment the measurement of emotions. The study explores the validity of sentiment analysis in psychotherapy transcripts. METHOD We used a transformer-based NLP algorithm to analyze sentiments in 85 transcripts from 35 patients. Construct and criterion validity were evaluated using self- and therapist reports and process and outcome measures via correlational, multitrait-multimethod, and multilevel analyses. RESULTS The results provide indications in support of the sentiments' validity. For example, sentiments were significantly related to self- and therapist reports of emotions in the same session. Sentiments correlated significantly with in-session processes (e.g., coping experiences), and an increase in positive sentiments throughout therapy predicted better outcomes after treatment termination. DISCUSSION Sentiment analysis could serve as a valid approach to assessing the emotional tone of psychotherapy sessions and may contribute to the multimodal measurement of emotions. Future research could combine sentiment analysis with automatic emotion recognition in facial expressions and vocal cues via the Nonverbal Behavior Analyzer (NOVA). Limitations (e.g., exploratory study with numerous tests) and opportunities are discussed.
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29
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Hanai A, Ishikawa T, Sugao S, Fujii M, Hirai K, Watanabe H, Matsuzaki M, Nakamoto G, Takeda T, Kitabatake Y, Itoh Y, Endo M, Kimura T, Kawakami E. Explainable Machine Learning Classification to Identify Vulnerable Groups Among Parenting Mothers: Web-Based Cross-Sectional Questionnaire Study. JMIR Form Res 2024; 8:e47372. [PMID: 38324356 PMCID: PMC10882468 DOI: 10.2196/47372] [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: 03/17/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND One life event that requires extensive resilience and adaptation is parenting. However, resilience and perceived support in child-rearing vary, making the real-world situation unclear, even with postpartum checkups. OBJECTIVE This study aimed to explore the psychosocial status of mothers during the child-rearing period from newborn to toddler, with a classifier based on data on the resilience and adaptation characteristics of mothers with newborns. METHODS A web-based cross-sectional survey was conducted. Mothers with newborns aged approximately 1 month (newborn cohort) were analyzed to construct an explainable machine learning classifier to stratify parenting-related resilience and adaptation characteristics and identify vulnerable populations. Explainable k-means clustering was used because of its high explanatory power and applicability. The classifier was applied to mothers with infants aged 2 months to 1 year (infant cohort) and mothers with toddlers aged >1 year to 2 years (toddler cohort). Psychosocial status, including depressed mood assessed by the Edinburgh Postnatal Depression Scale (EPDS), bonding assessed by the Postpartum Bonding Questionnaire (PBQ), and sleep quality assessed by the Pittsburgh Sleep Quality Index (PSQI) between the classified groups, was compared. RESULTS A total of 1559 participants completed the survey. They were split into 3 cohorts, comprising populations of various characteristics, including parenting difficulties and psychosocial measures. The classifier, which stratified participants into 5 groups, was generated from the self-reported scores of resilience and adaptation in the newborn cohort (n=310). The classifier identified that the group with the greatest difficulties in resilience and adaptation to a child's temperament and perceived support had higher incidences of problems with depressed mood (relative prevalence [RP] 5.87, 95% CI 2.77-12.45), bonding (RP 5.38, 95% CI 2.53-11.45), and sleep quality (RP 1.70, 95% CI 1.20-2.40) compared to the group with no difficulties in perceived support. In the infant cohort (n=619) and toddler cohort (n=461), the stratified group with the greatest difficulties had higher incidences of problems with depressed mood (RP 9.05, 95% CI 4.36-18.80 and RP 4.63, 95% CI 2.38-9.02, respectively), bonding (RP 1.63, 95% CI 1.29-2.06 and RP 3.19, 95% CI 2.03-5.01, respectively), and sleep quality (RP 8.09, 95% CI 4.62-16.37 and RP 1.72, 95% CI 1.23-2.42, respectively) compared to the group with no difficulties. CONCLUSIONS The classifier, based on a combination of resilience and adaptation to the child's temperament and perceived support, was able identify psychosocial vulnerable groups in the newborn cohort, the start-up stage of childcare. Psychosocially vulnerable groups were also identified in qualitatively different infant and toddler cohorts, depending on their classifier. The vulnerable group identified in the infant cohort showed particularly high RP for depressed mood and poor sleep quality.
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Affiliation(s)
- Akiko Hanai
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
- Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
- Institute for Datability Science, Osaka University, Suita, Japan
| | - Tetsuo Ishikawa
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
- Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
- Institute for Datability Science, Osaka University, Suita, Japan
- Department of Extended Intelligence for Medicine, The Ishii-Ishibashi Laboratory, Keio University School of Medicine, Tokyo, Japan
| | - Shoko Sugao
- Graduate School of Human Sciences, Osaka University, Suita, Japan
| | - Makoto Fujii
- Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kei Hirai
- Graduate School of Human Sciences, Osaka University, Suita, Japan
| | - Hiroko Watanabe
- Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Masayo Matsuzaki
- Department of Reproductive Health Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Goji Nakamoto
- Division of Health Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Toshihiro Takeda
- Department of Medical Informatics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Yasuji Kitabatake
- Department of Pediatrics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Yuichi Itoh
- Department of Integrated Information Technology, College of Science and Engineering, Aoyama Gakuin University, Sagamihara, Japan
| | - Masayuki Endo
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Tadashi Kimura
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Eiryo Kawakami
- Medical Data Mathematical Reasoning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
- Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
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30
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Li X, Chen F, Ma L. Exploring the Potential of Artificial Intelligence in Adolescent Suicide Prevention: Current Applications, Challenges, and Future Directions. Psychiatry 2024; 87:7-20. [PMID: 38227496 DOI: 10.1080/00332747.2023.2291945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
ObjectiveThe global surge in adolescent suicide necessitates the development of innovative and efficacious preventive measures. Traditionally, various approaches have been used, but with limited success. However, with the rapid advancements in artificial intelligence (AI), new possibilities have emerged. This paper reviews the potentials and challenges of integrating AI into suicide prevention strategies, focusing on adolescents. Method: This narrative review assesses the impact of AI on suicide prevention strategies, the strategies and cases of AI applications in adolescent suicide prevention, as well as the challenges faced. Through searches on the PubMed, web of science, PsycINFO, and EMBASE databases, 19 relevant articles were included in the review. Results: AI has significantly improved risk assessment and predictive modeling for identifying suicidal behavior. It has enabled the analysis of textual data through natural language processing and fostered novel intervention strategies. Although AI applications, such as chatbots and monitoring systems, show promise, they must navigate challenges like data privacy and ethical considerations. The research underscores the potential of AI to enhance future suicide prevention efforts through personalized interventions and integration with emerging technologies. Conclusion: AI possesses transformative potential for adolescent suicide prevention by offering targeted and adaptive solutions, while they also raise crucial ethical and practical considerations. Looking forward, AI can play a critical role in mitigating adolescent suicide rates, marking a new frontier in mental health care.
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31
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Benacek J, Lawal N, Ong T, Tomasik J, Martin-Key NA, Funnell EL, Barton-Owen G, Olmert T, Cowell D, Bahn S. Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach. JMIR Ment Health 2024; 11:e50738. [PMID: 38206660 PMCID: PMC10811571 DOI: 10.2196/50738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/27/2023] [Accepted: 12/01/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. OBJECTIVE In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. METHODS Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. RESULTS The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. CONCLUSIONS This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.
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Affiliation(s)
- Jiri Benacek
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | - Nimotalai Lawal
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | - Tommy Ong
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | - Jakub Tomasik
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | - Nayra A Martin-Key
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | - Erin L Funnell
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
- Psyomics Ltd, Cambridge, United Kingdom
| | | | - Tony Olmert
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
| | | | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, Cambridge Centre for Neuropsychiatric Research, University of Cambridge, Cambridge, United Kingdom
- Psyomics Ltd, Cambridge, United Kingdom
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Li E, Ai F, Liang C. A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study. Front Public Health 2024; 11:1348803. [PMID: 38259742 PMCID: PMC10800603 DOI: 10.3389/fpubh.2023.1348803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Objective Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model for depression, aiming to predict the probability of depression in the OSAHS population. Relevant features were analyzed and a nomogram was drawn to visually predict and easily estimate the risk of depression according to the best performing model. Study design This is a cross-sectional study. Methods Data from three cycles (2005-2006, 2007-2008, and 2015-2016) were selected from the NHANES database, and 16 influencing factors were screened and included. Three prediction models were established by the logistic regression algorithm, least absolute shrinkage and selection operator (LASSO) algorithm, and random forest algorithm, respectively. The receiver operating characteristic (ROC) area under the curve (AUC), specificity, sensitivity, and decision curve analysis (DCA) were used to assess evaluate and compare the different ML models. Results The logistic regression model had lower sensitivity than the lasso model, while the specificity and AUC area were higher than the random forest and lasso models. Moreover, when the threshold probability range was 0.19-0.25 and 0.45-0.82, the net benefit of the logistic regression model was the largest. The logistic regression model clarified the factors contributing to depression, including gender, general health condition, body mass index (BMI), smoking, OSAHS severity, age, education level, ratio of family income to poverty (PIR), and asthma. Conclusion This study developed three machine learning (ML) models (logistic regression model, lasso model, and random forest model) using the NHANES database to predict depression and identify influencing factors among OSAHS patients. Among them, the logistic regression model was superior to the lasso and random forest models in overall prediction performance. By drawing the nomogram and applying it to the sleep testing center or sleep clinic, sleep technicians and medical staff can quickly and easily identify whether OSAHS patients have depression to carry out the necessary referral and psychological treatment.
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Affiliation(s)
| | | | - Chunguang Liang
- Department of Nursing, Jinzhou Medical University, Jinzhou, China
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Hao Y, Zhang J, Yu J, Yu Z, Yang L, Hao X, Gao F, Zhou C. Predicting quetiapine dose in patients with depression using machine learning techniques based on real-world evidence. Ann Gen Psychiatry 2024; 23:5. [PMID: 38184628 PMCID: PMC10771703 DOI: 10.1186/s12991-023-00483-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/25/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Being one of the most widespread, pervasive, and troublesome illnesses in the world, depression causes dysfunction in various spheres of individual and social life. Regrettably, despite obtaining evidence-based antidepressant medication, up to 70% of people are going to continue to experience troublesome symptoms. Quetiapine, as one of the most commonly prescribed antipsychotic medication worldwide, has been reported as an effective augmentation strategy to antidepressants. The right quetiapine dose and personalized quetiapine treatment are frequently challenging for clinicians. This study aimed to identify important influencing variables for quetiapine dose by maximizing the use of data from real world, and develop a predictive model of quetiapine dose through machine learning techniques to support selections for treatment regimens. METHODS The study comprised 308 depressed patients who were medicated with quetiapine and hospitalized in the First Hospital of Hebei Medical University, from November 1, 2019, to August 31, 2022. To identify the important variables influencing the dose of quetiapine, a univariate analysis was applied. The prediction abilities of nine machine learning models (XGBoost, LightGBM, RF, GBDT, SVM, LR, ANN, DT) were compared. Algorithm with the optimal model performance was chosen to develop the prediction model. RESULTS Four predictors were selected from 38 variables by the univariate analysis (p < 0.05), including quetiapine TDM value, age, mean corpuscular hemoglobin concentration, and total bile acid. Ultimately, the XGBoost algorithm was used to create a prediction model for quetiapine dose that had the greatest predictive performance (accuracy = 0.69) out of nine models. In the testing cohort (62 cases), a total of 43 cases were correctly predicted of the quetiapine dose regimen. In dose subgroup analysis, AUROC for patients with daily dose of 100 mg, 200 mg, 300 mg and 400 mg were 0.99, 0.75, 0.93 and 0.86, respectively. CONCLUSIONS In this work, machine learning techniques are used for the first time to estimate the dose of quetiapine for patients with depression, which is valuable for the clinical drug recommendations.
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Affiliation(s)
- Yupei Hao
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Jing Yu
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Ze Yu
- Beijing Medicinovo Technology Co., Ltd, Beijing, China
| | - Lin Yang
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, China
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xin Hao
- Dalian Medicinovo Technology Co., Ltd, Dalian, China
| | - Fei Gao
- Beijing Medicinovo Technology Co., Ltd, Beijing, China.
| | - Chunhua Zhou
- Department of Clinical Pharmacy, the First Hospital of Hebei Medical University, Shijiazhuang, China.
- The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, China.
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Kong J, Zhang D. Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis. Front Neurol 2024; 14:1335851. [PMID: 38229595 PMCID: PMC10789857 DOI: 10.3389/fneur.2023.1335851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024] Open
Abstract
Background Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients. Methods We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients. Results Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS. Conclusion Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.
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Affiliation(s)
- Jinfen Kong
- Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China
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Idei H, Yamashita Y. Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models. Neural Netw 2024; 169:57-74. [PMID: 37857173 DOI: 10.1016/j.neunet.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.
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Affiliation(s)
- Hayato Idei
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan.
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Dominke C, Fischer AM, Grimmer T, Diehl-Schmid J, Jahn T. CERAD-NAB and flexible battery based neuropsychological differentiation of Alzheimer's dementia and depression using machine learning approaches. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2024; 31:221-248. [PMID: 36320158 DOI: 10.1080/13825585.2022.2138255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
Depression (DEP) and dementia of the Alzheimer's type (DAT) represent the most common neuropsychiatric disorders in elderly patients. Accurate differential diagnosis is indispensable to ensure appropriate treatment. However, DEP can yet mimic cognitive symptoms of DAT and patients with DAT often also present with depressive symptoms, impeding correct diagnosis. Machine learning (ML) approaches could eventually improve this discrimination using neuropsychological test data, but evidence is still missing. We therefore employed Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF) and conventional Logistic Regression (LR) to retrospectively predict the diagnoses of 189 elderly patients (68 DEP and 121 DAT) based on either the well-established Consortium to Establish a Registry for Alzheimer's Disease neuropsychological assessment battery (CERAD-NAB) or a flexible battery approach (FLEXBAT). The best performing combination consisted of FLEXBAT and NB, correctly classifying 87.0% of patients as either DAT or DEP. However, all accuracies were similar across algorithms and test batteries (83.0% - 87.0%). Accordingly, our study is the first to show that common ML algorithms with their default parameters can accurately differentiate between patients clinically diagnosed with DAT or DEP using neuropsychological test data, but do not necessarily outperform conventional LR.
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Affiliation(s)
- Clara Dominke
- Division Clinical Neuropsychology, Department of Psychology, Ludwig-Maximilians-University, Munich, Germany
| | - Alina Maria Fischer
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
| | - Janine Diehl-Schmid
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
- Centre for Geriatric Medicine, Kbo-Inn-Salzach-Klinikum, Wasserburg am Inn, Germany
| | - Thomas Jahn
- Division Clinical Neuropsychology, Department of Psychology, Ludwig-Maximilians-University, Munich, Germany
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
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Zhang Y, Hu M, Zhao W, Liu X, Peng Q, Meng B, Yang S, Feng X, Zhang L. A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care. J Neurol Surg A Cent Eur Neurosurg 2024; 85:62-73. [PMID: 36640757 DOI: 10.1055/a-2013-3149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders. METHODS On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping. RESULTS A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. CONCLUSIONS AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.
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Affiliation(s)
- Yu Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Man Hu
- Graduate School of Dalian Medical University, Dalian, China
| | - Wenjie Zhao
- Graduate School of Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Qing Peng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Bo Meng
- Graduate School of Dalian Medical University, Dalian, China
| | - Sheng Yang
- Graduate School of Dalian Medical University, Dalian, China
| | - Xinmin Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
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Siarkos K, Karavasilis E, Velonakis G, Papageorgiou C, Smyrnis N, Kelekis N, Politis A. Brain multi-contrast, multi-atlas segmentation of diffusion tensor imaging and ensemble learning automatically diagnose late-life depression. Sci Rep 2023; 13:22743. [PMID: 38123613 PMCID: PMC10733280 DOI: 10.1038/s41598-023-49935-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
We investigated the potential of machine learning for diagnostic classification in late-life major depression based on an advanced whole brain white matter segmentation framework. Twenty-six late-life depression and 12 never depressed individuals aged > 55 years, matched for age, MMSE, and education underwent brain diffusion tensor imaging and a multi-contrast, multi-atlas segmentation in MRIcloud. Fractional anisotropy volume, mean fractional anisotropy, trace, axial and radial diffusivity (RD) extracted from 146 white matter parcels for each subject were used to train and test the AdaBoost classifier using stratified 12-fold cross validation. Performance was evaluated using various measures. The statistical power of the classifier was assessed using label permutation test. Statistical analysis did not yield significant differences in DTI measures between the groups. The classifier achieved a balanced accuracy of 71% and an Area Under the Receiver Operator Characteristic Curve (ROC-AUC) of 0.81 by trace, and a balanced accuracy of 70% and a ROC-AUC of 0.80 by RD, in limbic, cortico-basal ganglia-thalamo-cortical loop, brainstem, external and internal capsules, callosal and cerebellar structures. Both indices shared important structures for classification, while fornix was the most important structure for classification by both indices. The classifier proved statistically significant, as trace and RD ROC-AUC scores after permutation were lower than those obtained with the actual data (P = 0.022 and P = 0.024, respectively). Similar results were obtained with the Gradient Boosting classifier, whereas the RBF-kernel Support Vector Machine with k-best feature selection did not exceed the chance level. Finally, AdaBoost significantly predicted the class using all features together. Limitations are discussed. The results encourage further investigation of the implemented methods for computer aided diagnostics and anatomically informed therapeutics.
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Affiliation(s)
- Kostas Siarkos
- Division of Geriatric Psychiatry, First Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece.
| | - Efstratios Karavasilis
- Medical School, Democritus University of Thrace, Alexandroupolis, Greece
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Georgios Velonakis
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Charalabos Papageorgiou
- University Mental Health, Neurosciences and Precision Medicine Research Institute "Costas Stefanis", Athens, Greece
| | - Nikolaos Smyrnis
- Second Department of Psychiatry, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Nikolaos Kelekis
- Second Department of Radiology, Attikon General University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Antonios Politis
- Division of Geriatric Psychiatry, First Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
- Department of Psychiatry, Division of Geriatric Psychiatry and Neuropsychiatry, Johns Hopkins Medical School, Baltimore, USA
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Price GD, Heinz MV, Song SH, Nemesure MD, Jacobson NC. Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data. Transl Psychiatry 2023; 13:381. [PMID: 38071317 PMCID: PMC10710399 DOI: 10.1038/s41398-023-02669-y] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/02/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.
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Affiliation(s)
- George D Price
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA.
- Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, USA.
| | - Michael V Heinz
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
| | - Seo Ho Song
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Matthew D Nemesure
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, USA
- Digital Data Design Institute, Harvard Business School, Harvard University, Cambridge, MA, USA
| | - Nicholas C Jacobson
- Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Dartmouth College, Lebanon, NH, USA
- Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, USA
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Haque UM, Kabir E, Khanam R. Early detection of paediatric and adolescent obsessive-compulsive, separation anxiety and attention deficit hyperactivity disorder using machine learning algorithms. Health Inf Sci Syst 2023; 11:31. [PMID: 37489154 PMCID: PMC10363094 DOI: 10.1007/s13755-023-00232-z] [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: 01/24/2023] [Accepted: 07/04/2023] [Indexed: 07/26/2023] Open
Abstract
Purpose Mental health issues of young minds are at the threshold of all development and possibilities. Obsessive-compulsive disorder (OCD), separation anxiety disorder (SAD), and attention deficit hyperactivity disorder (ADHD) are three of the most common mental illness affecting children and adolescents. Several studies have been conducted on approaches for recognising OCD, SAD and ADHD, but their accuracy is inadequate due to limited features and participants. Therefore, the purpose of this study is to investigate the approach using machine learning (ML) algorithms with 1474 features from Australia's nationally representative mental health survey of children and adolescents. Methods Based on the internal cross-validation (CV) score of the Tree-based Pipeline Optimization Tool (TPOTClassifier), the dataset has been examined using three of the most optimal algorithms, including Random Forest (RF), Decision Tree (DT), and Gaussian Naïve Bayes (GaussianNB). Results GaussianNB performs well in classifying OCD with 91% accuracy, 76% precision, and 96% specificity as well as in detecting SAD with 79% accuracy, 62% precision, 91% specificity. RF outperformed all other methods in identifying ADHD with 91% accuracy, 94% precision, and 99% specificity. Conclusion Using Streamlit and Python a web application was developed based on the findings of the analysis. The application will assist parents/guardians and school officials in detecting mental illnesses early in their children and adolescents using signs and symptoms to start the treatment at the earliest convenience.
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Affiliation(s)
- Umme Marzia Haque
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Enamul Kabir
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia
| | - Rasheda Khanam
- School of Business, University of Southern Queensland, Toowoomba, Australia
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Zantvoort K, Scharfenberger J, Boß L, Lehr D, Funk B. Finding the Best Match - a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2023; 7:447-479. [PMID: 37927375 PMCID: PMC10620349 DOI: 10.1007/s41666-023-00148-z] [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: 09/30/2022] [Accepted: 08/29/2023] [Indexed: 11/07/2023]
Abstract
With the need for psychological help long exceeding the supply, finding ways of scaling, and better allocating mental health support is a necessity. This paper contributes by investigating how to best predict intervention dropout and failure to allow for a need-based adaptation of treatment. We systematically compare the predictive power of different text representation methods (metadata, TF-IDF, sentiment and topic analysis, and word embeddings) in combination with supplementary numerical inputs (socio-demographic, evaluation, and closed-question data). Additionally, we address the research gap of which ML model types - ranging from linear to sophisticated deep learning models - are best suited for different features and outcome variables. To this end, we analyze nearly 16.000 open-text answers from 849 German-speaking users in a Digital Mental Health Intervention (DMHI) for stress. Our research proves that - contrary to previous findings - there is great promise in using neural network approaches on DMHI text data. We propose a task-specific LSTM-based model architecture to tackle the challenge of long input sequences and thereby demonstrate the potential of word embeddings (AUC scores of up to 0.7) for predictions in DMHIs. Despite the relatively small data set, sequential deep learning models, on average, outperform simpler features such as metadata and bag-of-words approaches when predicting dropout. The conclusion is that user-generated text of the first two sessions carries predictive power regarding patients' dropout and intervention failure risk. Furthermore, the match between the sophistication of features and models needs to be closely considered to optimize results, and additional non-text features increase prediction results. Supplementary Information The online version contains supplementary material available at 10.1007/s41666-023-00148-z.
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Affiliation(s)
- Kirsten Zantvoort
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
| | | | - Leif Boß
- Institute of Psychology, Leuphana University, Lüneburg, Germany
| | - Dirk Lehr
- Institute of Psychology, Leuphana University, Lüneburg, Germany
| | - Burkhardt Funk
- Institute of Information Systems, Leuphana University, Lüneburg, Germany
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Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Med Inform Decis Mak 2023; 23:271. [PMID: 38012655 PMCID: PMC10680172 DOI: 10.1186/s12911-023-02341-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression. METHODS Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022). RESULTS Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. LIMITATIONS The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography. CONCLUSION This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.
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Affiliation(s)
| | - Caroline Meyer
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Lukasz Walasek
- Department of Psychology, University of Warwick, Coventry, UK
| | - Carla Toro
- Warwick Medical School, University of Warwick, Coventry, UK
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Wang M, Richmond LL, Schleider JL, Nelson BD, Luhmann CC. Predicting internalizing symptoms with machine learning: identifying individuals that need care. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023:1-10. [PMID: 37943500 DOI: 10.1080/07448481.2023.2277185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 10/22/2023] [Indexed: 11/10/2023]
Abstract
Objective The current project aims to identify individuals in urgent need of mental health care, using a machine learning algorithm (random forest). Comparison/contrast with conventional regression analyses is discussed. Participants: A total of 2,409 participants were recruited from an anonymous university, including undergraduate and graduate students, faculty, and staff. Methods: Answers to a COVID-19 impact survey, the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were collected. The total scores of PHQ-9 and GAD-7 were regressed on six composites that were created from the questionnaire items, based on their topics. A random forest was trained and validated. Results: Results indicate that the random forest model was able to make accurate, prospective predictions (R2 = .429 on average) and we review variables that were deemed predictively relevant. Conclusions: Overall, the study suggests that predictive models can be clinically useful in identifying individuals with internalizing symptoms based on daily life disruption experiences.
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Affiliation(s)
- Mengxing Wang
- Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Lauren L Richmond
- Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Jessica L Schleider
- Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Brady D Nelson
- Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Christian C Luhmann
- Department of Psychology, Stony Brook University, Stony Brook, New York, USA
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Vaid A, Sawant A, Suarez-Farinas M, Lee J, Kaul S, Kovatch P, Freeman R, Jiang J, Jayaraman P, Fayad Z, Argulian E, Lerakis S, Charney AW, Wang F, Levin M, Glicksberg B, Narula J, Hofer I, Singh K, Nadkarni GN. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study. Ann Intern Med 2023; 176:1358-1369. [PMID: 37812781 DOI: 10.7326/m23-0949] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS 130 000 critical care admissions across both health systems. INTERVENTION Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE National Center for Advancing Translational Sciences.
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Affiliation(s)
- Akhil Vaid
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Ashwin Sawant
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.S.)
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Juhee Lee
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Sanjeev Kaul
- Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (S.K.)
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Robert Freeman
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (R.F.)
| | - Joy Jiang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (J.J.)
| | - Pushkala Jayaraman
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Zahi Fayad
- BioMedical Engineering and Imaging Institute and Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (Z.F.)
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, and Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (A.W.C.)
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York (F.W.)
| | - Matthew Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (M.L.)
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Ira Hofer
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York (I.H.)
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan (K.S.)
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (G.N.N.)
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Hawes MT, Schwartz HA, Son Y, Klein DN. Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning. Psychol Med 2023; 53:6205-6211. [PMID: 36377499 DOI: 10.1017/s0033291722003452] [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] [Indexed: 11/16/2022]
Abstract
BACKGROUND This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence. METHODS A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3-15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity). RESULTS CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics. CONCLUSIONS These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history.
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Affiliation(s)
- Mariah T Hawes
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - H Andrew Schwartz
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Youngseo Son
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
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Mosavi NS, Ribeiro E, Sampaio A, Santos MF. Data mining techniques in psychotherapy: applications for studying therapeutic alliance. Sci Rep 2023; 13:16409. [PMID: 37775524 PMCID: PMC10541430 DOI: 10.1038/s41598-023-43366-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 09/22/2023] [Indexed: 10/01/2023] Open
Abstract
Therapeutic Alliance (TA) has been consistently reported as a robust predictor of therapy outcomes and is one of the most investigated therapy relational factors. Research on therapists' and clients' contributions to the alliance development and the alliance-outcome relationship had shown mixed results. The relation of the therapist's and client's biological markers with the alliance is an important and under-investigated topic. Taking advantage of data mining techniques, this exploratory study aimed to investigate the role of different therapist and client factors, including heart rate (HR) and electrodermal activity (EDA), in relation to TA. Twenty-two dyads with 6 therapists and 22 clients participated in the study. The Working Alliance Inventory (WAI) was used to evaluate the client's and therapist's perception of the alliance at the end of each session and through the therapy processes. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to explore patterns that may contribute to TA. Machine Learning (ML) models have been employed to provide insights into the predictors and correlates of TA. Our results showed that Linear Regression (LR) was the best technique for predicting the therapist's TA, with client "Diagnostic" and therapy "Termination" being identified as significant predictors of the therapist's TA. In addition, for clients' TA, the Random Forest (RF) was shown to have the best performance. The therapist's TA and therapy "Outcome" were observed as the most influential predictors for the client's TA. In addition, while the Heart Rate (therapist) was negatively associated with the therapist's TA, EDA in the client was a physiological indicator related to the client's TA. Overall, these findings can assist in identifying key factors that therapists should focus on to enhance the quality of therapeutic alliance. Results are discussed in terms of their consistency with empirical literature, innovative and interdisciplinary research on the therapeutic alliance field, and, in particular, the use of the Data Mining approach in a psychotherapy context.
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Affiliation(s)
| | - Eugénia Ribeiro
- Psychotherapy and Psychopathology Research Lab, Centre for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Adriana Sampaio
- Psychological Neuroscience Lab, Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal
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Franken K, ten Klooster P, Bohlmeijer E, Westerhof G, Kraiss J. Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach. Front Psychiatry 2023; 14:1236551. [PMID: 37817829 PMCID: PMC10560743 DOI: 10.3389/fpsyt.2023.1236551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 09/11/2023] [Indexed: 10/12/2023] Open
Abstract
Objectives Anxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare. Methods In the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data. Results ML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models' performance (AUC range: 0.68-0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58-0.62 vs. 0.58-0.63) and models with early change scores (AUC: 0.69-0.73 vs. 0.68-0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement. Conclusion Accurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.
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Affiliation(s)
- Katinka Franken
- Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, Netherlands
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Qasrawi R, Hoteit M, Tayyem R, Bookari K, Al Sabbah H, Kamel I, Dashti S, Allehdan S, Bawadi H, Waly M, Ibrahim MO, Polo SV, Al-Halawa DA. Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic. BMC Public Health 2023; 23:1805. [PMID: 37716999 PMCID: PMC10505318 DOI: 10.1186/s12889-023-16694-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. RESULTS The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. CONCLUSIONS The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Jerusalem, Palestine.
- Department of Computer Engineering, Istinye University, Istanbul, 34010, Turkey.
| | - Maha Hoteit
- Faculty of Public Health, Lebanese University, Beirut, Lebanon
- PHENOL Research Group (Public Health Nutrition Program Lebanon), Faculty of Public Health, Lebanese University, Beirut, Lebanon
- Lebanese University Nutrition Surveillance Center (LUNSC), Lebanese Food Drugs and Chemical Administrations, Lebanese University, Beirut, Lebanon
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar
- Department of Nutrition and Food Technology, Faculty of Agriculture, University of Jordan, Amman, 11942, Jordan
| | - Khlood Bookari
- National Nutrition Committee, Saudi Food and Drug Authority, Riyadh, Saudi Arabia
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Haleama Al Sabbah
- Department of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates
| | | | - Somaia Dashti
- Public Authority for Applied Education and Training, Kuwait City, Kuwait
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Hiba Bawadi
- Department of Human Nutrition, College of Health Sciences, QU-Health, Qatar University, Doha, Qatar
| | - Mostafa Waly
- Food Science and Nutrition Department, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
| | - Mohammed O Ibrahim
- Department of Nutrition and Food Technology, Faculty of Agriculture, Mu'tah University, Karak, Jordan
| | | | - Diala Abu Al-Halawa
- Department of Faculty of Medicine, Al Quds University, Jerusalem, Palestine.
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Ebrahimi A, Wiil UK, Baskaran R, Peimankar A, Andersen K, Nielsen AS. AUD-DSS: a decision support system for early detection of patients with alcohol use disorder. BMC Bioinformatics 2023; 24:329. [PMID: 37658294 PMCID: PMC10474761 DOI: 10.1186/s12859-023-05450-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 08/21/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients' AUD status earlier. METHODS This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. RESULTS The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. CONCLUSION It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.
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Affiliation(s)
- Ali Ebrahimi
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
| | - Uffe Kock Wiil
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Ruben Baskaran
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Abdolrahman Peimankar
- SDU Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Kjeld Andersen
- Unit for Clinical Alcohol Research, Clinical Institute, University of Southern Denmark, Odense, Denmark
| | - Anette Søgaard Nielsen
- Unit for Clinical Alcohol Research, Clinical Institute, University of Southern Denmark, Odense, Denmark
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Cohen JR, Stutts M. Interpersonal Well-Being and Suicidal Outcomes in a Nationally Representative Study of Adolescents: A Translational Study. Res Child Adolesc Psychopathol 2023; 51:1327-1341. [PMID: 37222862 DOI: 10.1007/s10802-023-01068-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/18/2023] [Indexed: 05/25/2023]
Abstract
Adolescent suicide continues to rise despite burgeoning research on interpersonal risk for suicide. This may reflect challenges in applying developmental psychopathology research into clinical settings. In response, the present study used a translational analytic plan to examine indices of social well-being most accurate and statistically fair for indexing adolescent suicide. Data from the National Comorbidity Survey Replication Adolescent Supplement were used. Adolescents aged 13-17 (N = 9,900) completed surveys on traumatic events, current relationships, and suicidal thoughts and attempts. Both frequentist (e.g., receiver operating characteristics) and Bayesian (e.g., Diagnostic Likelihood Ratios; DLRs) techniques provided insight into classification, calibration, and statistical fairness. Final algorithms were compared to a machine learning-informed algorithm. Overall, parental care and family cohesion best classified suicidal ideation, while these indices and school engagement best classified attempts. Multi-indicator algorithms suggested adolescents at high risk across these indices were approximately 3-times more likely to engage in ideation (DLR = 3.26) and 5-times more likely to engage in attempts (DLR = 4.53). Although equitable for attempts, models for ideation underperformed in non-White adolescents. Supplemental, machine learning-informed algorithms performed similarly, suggesting non-linear and interactive effects did not improve model performance. Future directions for interpersonal theories for suicide are discussed and clinical implications for suicide screening are demonstrated.
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Affiliation(s)
- Joseph R Cohen
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA.
| | - Morgan Stutts
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, 61820, USA
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