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Bader M, Abdelwanis M, Maalouf M, Jelinek HF. Detecting depression severity using weighted random forest and oxidative stress biomarkers. Sci Rep 2024; 14:16328. [PMID: 39009760 PMCID: PMC11250802 DOI: 10.1038/s41598-024-67251-y] [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: 03/01/2024] [Accepted: 07/09/2024] [Indexed: 07/17/2024] Open
Abstract
This study employs machine learning to detect the severity of major depressive disorder (MDD) through binary and multiclass classifications. We compared models that used only biomarkers of oxidative stress with those that incorporate sociodemographic and health-related factors. Data collected from 830 participants, based on the Patient Health Questionnaire (PHQ-9) score, inform our analysis. In binary classification, the Random Forest (RF) classifier achieved the highest Area Under the Curve (AUC) of 0.84 when all features were included. In multiclass classification, the AUC improved from 0.84 with only oxidative stress biomarkers to 0.88 when all characteristics were included. To address data imbalance, weighted classifiers, and Synthetic Minority Over-sampling Technique (SMOTE) approaches were applied. Weighted random forest (WRF) improved multiclass classification, achieving an AUC of 0.91. Statistical tests, including the Friedman test and the Conover post-hoc test, confirmed significant differences between model performances, with WRF using all features outperforming others. Feature importance analysis shows that oxidative stress biomarkers, particularly GSH, are top ranked among all features. Clinicians can leverage the results of this study to improve their decision-making processes by incorporating oxidative stress biomarkers in addition to the standard criteria for depression diagnosis.
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Affiliation(s)
- Mariam Bader
- Department of Management Science and Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Moustafa Abdelwanis
- Department of Management Science and Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
| | - Maher Maalouf
- Department of Management Science and Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates.
| | - Herbert F Jelinek
- Department of Medical Science, Biotechnology Center, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates
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Bari S, Kim BW, Vike NL, Lalvani S, Stefanopoulos L, Maglaveras N, Block M, Strawn J, Katsaggelos AK, Breiter HC. A novel approach to anxiety level prediction using small sets of judgment and survey variables. NPJ MENTAL HEALTH RESEARCH 2024; 3:29. [PMID: 38890545 PMCID: PMC11189415 DOI: 10.1038/s44184-024-00074-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 05/16/2024] [Indexed: 06/20/2024]
Abstract
Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables (n = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2-3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29-31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.
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Affiliation(s)
- Sumra Bari
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Byoung-Woo Kim
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Nicole L Vike
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
| | - Shamal Lalvani
- Department of Electrical Engineering, Northwestern University, Evanston, IL, USA
| | - Leandros Stefanopoulos
- Department of Electrical Engineering, Northwestern University, Evanston, IL, USA
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicos Maglaveras
- Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Martin Block
- Integrated Marketing Communications, Medill School of Journalism, Northwestern University, Evanston, IL, USA
| | - Jeffrey Strawn
- Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
| | - Aggelos K Katsaggelos
- Department of Electrical Engineering, Northwestern University, Evanston, IL, USA
- Department of Computer Science, Northwestern University, Evanston, IL, USA
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Hans C Breiter
- Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA.
- Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA.
- Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA, USA.
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Richter T, Stahi S, Mirovsky G, Hel-Or H, Okon-Singer H. Disorder-specific versus transdiagnostic cognitive mechanisms in anxiety and depression: Machine-learning-based prediction of symptom severity. J Affect Disord 2024; 354:473-482. [PMID: 38479515 DOI: 10.1016/j.jad.2024.03.035] [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: 08/22/2023] [Revised: 03/03/2024] [Accepted: 03/09/2024] [Indexed: 03/25/2024]
Abstract
INTRODUCTION Psychiatric evaluation of anxiety and depression is currently based on self-reported symptoms and their classification into discrete disorders. Yet the substantial overlap between these disorders as well as their within-disorder heterogeneity may contribute to the mediocre success rates of treatments. The proposed research examines a new framework for diagnosis that is based on alterations in underlying cognitive mechanisms. In line with the Research Domain Criteria (RDoC) approach, the current study directly compares disorder-specific and transdiagnostic cognitive patterns in predicting the severity of anxiety and depression symptoms. METHODS The sample included 237 individuals exhibiting differing levels of anxiety and depression symptoms, as measured by the STAI-T and BDI-II. Random Forest regressors were used to analyze their performance on a battery of six computerized cognitive-behavioral tests targeting selective and spatial attention, expectancy, interpretation, memory, and cognitive control biases. RESULTS Unique anxiety-specific biases were found, as well as shared anxious-depressed bias patterns. These cognitive biases exhibited relatively high fitting rates when predicting symptom severity (questionnaire scores common range 0-60, MAE = 6.03, RMSE = 7.53). Interpretation and expectancy biases exhibited the highest association with symptoms, above all other individual biases. LIMITATIONS Although internal validation methods were applied, models may suffer from potential overfitting due to sample size limitations. CONCLUSION In the context of the ongoing dispute regarding symptom-centered versus transdiagnostic approaches, the current study provides a unique comparison of these two views, yielding a novel intermediate approach. The results support the use of mechanism-based dimensional diagnosis for adding precision and objectivity to future psychiatric evaluations.
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Affiliation(s)
- Thalia Richter
- School of Psychological Sciences, University of Haifa, Mount Carmel Haifa, Israel.
| | - Shahar Stahi
- Department of Computer Science, University of Haifa, Mount Carmel Haifa, Israel
| | - Gal Mirovsky
- Department of Computer Science, University of Haifa, Mount Carmel Haifa, Israel
| | - Hagit Hel-Or
- Department of Computer Science, University of Haifa, Mount Carmel Haifa, Israel
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, Mount Carmel Haifa, Israel; The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Mount Carmel Haifa, Israel
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Wang T, Xue C, Zhang Z, Cheng T, Yang G. Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships. Comput Biol Med 2024; 174:108446. [PMID: 38631118 DOI: 10.1016/j.compbiomed.2024.108446] [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: 12/19/2023] [Revised: 03/01/2024] [Accepted: 04/07/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Depression and anxiety, prevalent coexisting mood disorders, pose a clinical challenge in accurate differentiation, hindering effective healthcare interventions. This research addressed this gap by employing a streamlined Symptom Checklist 90 (SCL-90) designed to minimize patient response burden. Utilizing machine learning algorithms, the study sought to construct classification models capable of distinguishing between depression and anxiety. METHODS The study included 4262 individuals currently experiencing depression alone (n = 2998), anxiety alone (n = 716), or both depression and anxiety (n = 548). Counterfactual diagnosis was used to construct a causal network on the dataset. Employing a causal network, the SCL-90 was simplified. Items that have causality with only depression, only anxiety and both depression and anxiety were selected, and these streamlined items served as input features for four distinct machine learning algorithms, facilitating the creation of classification models for distinguishing depression and anxiety. RESULTS Cross-validation demonstrated the performance of the classification models with the following metrics: (1) K-nearest neighbors (AUC = 0.924, Acc = 92.81 %); (2) support vector machine (AUC = 0.937, Acc = 94.38 %); (3) random forest (AUC = 0.918, Acc = 94.38 %); and (4) adaptive boosting (AUC = 0.882, Acc = 94.38 %). Notably, the support vector machine excelled, with the highest AUC and superior accuracy. CONCLUSION Incorporating the simplified SCL-90 and machine learning presents a promising, efficient, and cost-effective tool for the precise identification of depression and anxiety.
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Affiliation(s)
- Tiantian Wang
- Department of Oncology, National Clinical Research Center for Geriatric Disorders (Xiangya Center), Xiangya Hospital, Central South University, Changsha, China; Changsha Social Laboratory of Artificial Intelligence, Changsha, China; School of Science, Hunan University of Technology and Business, Changsha, China
| | - Chuang Xue
- Department of Physiotherapy Treatment Center, Affiliated Mental Health Center &Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zijian Zhang
- Department of Oncology, National Clinical Research Center for Geriatric Disorders (Xiangya Center), Xiangya Hospital, Central South University, Changsha, China
| | - Tingting Cheng
- Department of General Medicine, National Clinical Research Center for Geriatric Disorders (Xiangya Center), Xiangya Hospital, Central South University, Changsha, China.
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, London, W12 7SL, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK; Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; School of Biomedical Engineering & Imaging Sciences, King's College London, London, WC2R 2LS, UK
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Chadaga K, Prabhu S, Sampathila N, Chadaga R, Bhat D, Sharma AK, Swathi KS. SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques. SLAS Technol 2024; 29:100129. [PMID: 38508237 DOI: 10.1016/j.slast.2024.100129] [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: 01/11/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the "Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the "Liebowitz Social Anxiety Scale questionnaire" and "The fear of speaking in public" are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.
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Affiliation(s)
- Krishnaraj Chadaga
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Srikanth Prabhu
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
| | - Rajagopala Chadaga
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Devadas Bhat
- Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Akhilesh Kumar Sharma
- Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
| | - K S Swathi
- Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
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Bhadra S, Kumar CJ. Enhancing the efficacy of depression detection system using optimal feature selection from EHR. Comput Methods Biomech Biomed Engin 2024; 27:222-236. [PMID: 36820618 DOI: 10.1080/10255842.2023.2181660] [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: 05/25/2022] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Diagnosing depression at an early stage is crucial and majorly depends on the clinician's skill. The present work aims to develop an automated tool for assisting the diagnostic procedure of depression using multiple machine-learning techniques. The dataset of sample size 4184 used in this study contains biometric and demographic information of individuals with or without depression, accessed from the University of Nice Sophia-Antipolis. The Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are used for classifying the depressed from the control group. To enhance the computational efficiency, various feature selection algorithms like Recursive Feature Elimination (RFE), Mutual Information (MI) and three bio-inspired techniques, viz. Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Firefly Algorithms (FA) have been incorporated. To enhance the feature selection process further, majority voting is carried out in all possible combinations of three, four and five feature selection techniques. These feature selection techniques bring down the feature set size significantly to a mean of 33 from the actual size of 61 which is a reduction of 45.90%. The classification accuracy of the enhanced model varies between 84.18% and 88.46%, which is a significant improvement in performance as compared to the pre-existing models (83.76-85.89%). The proposed predictive models outperform the pre-existing classification models without feature selection and thereby enhancing both the performance and efficiency of the diagnostic process.
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Affiliation(s)
- Sweta Bhadra
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
| | - Chandan Jyoti Kumar
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
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7
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Abado E, Aue T, Okon-Singer H. Spider vs. guns: expectancy and attention biases to phylogenetic threat do not extend to ontogenetic threat. Front Psychol 2023; 14:1232985. [PMID: 37711323 PMCID: PMC10498540 DOI: 10.3389/fpsyg.2023.1232985] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction Attention bias plays an important role in specific fears and phobias. Previous studies revealed that a-priori expectancies affect attention toward neutral stimuli but not threatening stimuli. The aim of the current study was to test whether this selective influence of expectancies on attention is specific to phylogenetic threat (i.e., spiders) or whether it can be generalized to ontogenetic threat (i.e., guns). Correspondingly, we directly compared expectancy effects on attentional allocation to phylogenetically vs. ontogenetically threatening stimuli. Method Expectancies were manipulated by presenting a cue indicating the likelihood of the appearance of a deviant picture in a visual search array. The array included eight distractors and one neutral (phone/bird) or threatening (gun/spider) deviant picture. In a comprehensive design, we examined the effects of stimulus type (phylogenetic/ontogenetic) and visual background (white and sterile/complex and ecological). Individual differences such as intolerance of uncertainty and spider fear were also measured. Results Results showed that attention bias toward spiders does not extend to threatening ontogenetic stimuli (i.e., guns). Our previous findings on attention bias toward spiders were replicated and a small to medium positive correlation was found between reaction time to bird targets and pre-existing fear of spider levels. Cues were used to detect threatening as well as neutral targets on both background types, except for spider targets on a complex background, replicating previous results. A small to medium positive correlation was also found between fear of spiders and intolerance of uncertainty. Discussion Together, these results suggest that expectancy and attentional processes may differ between ontogenetic and phylogenetic threat. Importantly, the effects of expectancy on attentional allocation depend on an interaction between the type of threat (ontogenetic/phylogenetic), visual factors, and individual differences.
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Affiliation(s)
- Elinor Abado
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
| | - Tatjana Aue
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
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8
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Bague K, Laurent É. Depressive symptoms and affordance perception: The case of perceived reachability boundary. Psychon Bull Rev 2023; 30:1396-1409. [PMID: 36650363 DOI: 10.3758/s13423-022-02242-6] [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: 12/26/2022] [Indexed: 01/19/2023]
Abstract
According to the ecological approach to perception, affordances (i.e., action possibilities) are perceived directly in the environment and body-scaled. Previous theoretical work has suggested that the perception of action possibilities is influenced by depression (which has sometimes been conceived as an action-related disorder). However, thus far the relationship between affordance and depression has never been investigated in an experimental study. The goal of this study was to assess the relationships between reachability perception and depressive symptoms. Participants estimated their maximum ability to reach a target with their hand (without moving). Actual motor reachability capacities were then assessed. To determine the critical point, both measures were related to the participant's arm length and converted into an intrinsic body-scaled measurement. Participants were allocated to either the healthy group or the group with depressive symptoms according to their Beck Depression Inventory-Fast Screen (BDI-FS) scores. Results showed that participants with depressive symptoms were more conservative in their estimations than healthy participants. Depressive symptoms were associated with the perception of decreased motor action possibilities in comparison with what was observed when no symptoms were reported. These data are discussed in relation to theoretical models of depression and affordance.
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Shusharina N, Yukhnenko D, Botman S, Sapunov V, Savinov V, Kamyshov G, Sayapin D, Voznyuk I. Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression. Diagnostics (Basel) 2023; 13:diagnostics13030573. [PMID: 36766678 PMCID: PMC9914271 DOI: 10.3390/diagnostics13030573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/09/2023] Open
Abstract
This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.
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Affiliation(s)
- Natalia Shusharina
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Correspondence:
| | - Denis Yukhnenko
- Department of Social Security and Humanitarian Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
| | - Stepan Botman
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Viktor Sapunov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Vladimir Savinov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Gleb Kamyshov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Dmitry Sayapin
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Igor Voznyuk
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Department of Neurology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
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Abado E, Okon-Singer H, Aue T. Neurophysiological mechanisms underlying cognitive biases to emotional information: Latest developments and new directions. Biol Psychol 2023; 177:108486. [PMID: 36626959 DOI: 10.1016/j.biopsycho.2023.108486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Affiliation(s)
- Elinor Abado
- School of Psychological Sciences, University of Haifa, Haifa, Israel; The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel.
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, Haifa, Israel; The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
| | - Tatjana Aue
- Institute of Psychology, University of Bern, Bern, Switzerland
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11
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Hou T, Qiao W, Song S, Guan Y, Zhu C, Yang Q, Gu Q, Sun L, Liu S. The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients. Clin Appl Thromb Hemost 2023; 29:10760296231179438. [PMID: 37365805 DOI: 10.1177/10760296231179438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Rehabilitation is crucial to recovering patients' dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods. METHODS We analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models. RESULTS Artificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models. CONCLUSIONS Risk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.
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Affiliation(s)
- Tingting Hou
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
- Graduate School of Dalian Medical University, Dalian, Liaoning Province, China
| | - Wei Qiao
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
- Graduate School of Dalian Medical University, Dalian, Liaoning Province, China
| | - Sijin Song
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
- Graduate School of Dalian Medical University, Dalian, Liaoning Province, China
| | - Yingchao Guan
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
- Graduate School of Dalian Medical University, Dalian, Liaoning Province, China
| | - Chunyang Zhu
- School of Sciences, Nantong University, Nantong, Jiangsu Province, China
| | - Qing Yang
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
- Graduate School of Dalian Medical University, Dalian, Liaoning Province, China
| | - Qi Gu
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | - Li Sun
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
| | - Su Liu
- Department of Rehabilitation Medicine, Affiliated Hospital of Nantong University, Nantong, China
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A multilevel person-centered examination of students' learning anxiety and its relationship with student background and school factors. LEARNING AND INDIVIDUAL DIFFERENCES 2023. [DOI: 10.1016/j.lindif.2022.102253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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13
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Nadeem A, Naveed M, Islam Satti M, Afzal H, Ahmad T, Kim KI. Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249775. [PMID: 36560144 PMCID: PMC9782829 DOI: 10.3390/s22249775] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/30/2022] [Accepted: 12/08/2022] [Indexed: 06/01/2023]
Abstract
In today's world, mental health diseases have become highly prevalent, and depression is one of the mental health problems that has become widespread. According to WHO reports, depression is the second-leading cause of the global burden of diseases. In the proliferation of such issues, social media has proven to be a great platform for people to express themselves. Thus, a user's social media can speak a great deal about his/her emotional state and mental health. Considering the high pervasiveness of the disease, this paper presents a novel framework for depression detection from textual data, employing Natural Language Processing and deep learning techniques. For this purpose, a dataset consisting of tweets was created, which were then manually annotated by the domain experts to capture the implicit and explicit depression context. Two variations of the dataset were created, on having binary and one ternary labels, respectively. Ultimately, a deep-learning-based hybrid Sequence, Semantic, Context Learning (SSCL) classification framework with a self-attention mechanism is proposed that utilizes GloVe (pre-trained word embeddings) for feature extraction; LSTM and CNN were used to capture the sequence and semantics of tweets; finally, the GRUs and self-attention mechanism were used, which focus on contextual and implicit information in the tweets. The framework outperformed the existing techniques in detecting the explicit and implicit context, with an accuracy of 97.4 for binary labeled data and 82.9 for ternary labeled data. We further tested our proposed SSCL framework on unseen data (random tweets), for which an F1-score of 94.4 was achieved. Furthermore, in order to showcase the strengths of the proposed framework, we validated it on the "News Headline Data set" for sarcasm detection, considering a dataset from a different domain. It also outmatched the performance of existing techniques in cross-domain validation.
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Affiliation(s)
- Aleena Nadeem
- Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Muhammad Naveed
- Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
| | - Muhammad Islam Satti
- Department of Computer Science, Faculty of Computing, Riphah International University, Islamabad 46000, Pakistan
| | - Hammad Afzal
- Department of Computer Software Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan
| | - Tanveer Ahmad
- Innovation Education and Research Center for On-Device AI Software (Bk21), Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Ki-Il Kim
- Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
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Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Med Inform 2022; 10:e38943. [PMID: 36040777 PMCID: PMC9472035 DOI: 10.2196/38943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/11/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Anxiety is one of the leading causes of mental health disability around the world. Currently, a majority of the population who experience anxiety go undiagnosed or untreated. New and innovative ways of diagnosing and monitoring anxiety have emerged using smartphone sensor-based monitoring as a metric for the management of anxiety. This is a novel study as it adds to the field of research through the use of nonidentifiable smartphone usage to help detect and monitor anxiety remotely and in a continuous and passive manner. OBJECTIVE This study aims to evaluate the accuracy of a novel mental behavioral profiling metric derived from smartphone usage for the identification and tracking of generalized anxiety disorder (GAD). METHODS Smartphone data and self-reported 7-item GAD anxiety assessments were collected from 229 participants using an Android operating system smartphone in an observational study over an average of 14 days (SD 29.8). A total of 34 features were mined to be constructed as a potential digital phenotyping marker from continuous smartphone usage data. We further analyzed the correlation of these digital behavioral markers against each item of the 7-item Generalized Anxiety Disorder Scale (GAD-7) and its influence on the predictions of machine learning algorithms. RESULTS A total of 229 participants were recruited in this study who had completed the GAD-7 assessment and had at least one set of passive digital data collected within a 24-hour period. The mean GAD-7 score was 11.8 (SD 5.7). Regression modeling was tested against classification modeling and the highest prediction accuracy was achieved from a binary XGBoost classification model (precision of 73%-81%; recall of 68%-87%; F1-score of 71%-79%; accuracy of 76%; area under the curve of 80%). Nonparametric permutation testing with Pearson correlation results indicated that the proposed metric (Mental Health Similarity Score [MHSS]) had a colinear relationship between GAD-7 Items 1, 3 and 7. CONCLUSIONS The proposed MHSS metric demonstrates the feasibility of using passively collected nonintrusive smartphone data and machine learning-based data mining techniques to track an individuals' daily anxiety levels with a 76% accuracy that directly relates to the GAD-7 scale.
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Affiliation(s)
- Soumya Choudhary
- Department of Research, Behavidence, Inc., New York, NY, United States
| | - Nikita Thomas
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Sultan Alshamrani
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Girish Srinivasan
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | | | - Usman Nawaz
- Department of Data Science, Behavidence, Inc., New York, NY, United States
| | - Roy Cohen
- Department of Research, Behavidence, Inc., New York, NY, United States
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15
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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16
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Qasrawi R, Amro M, VicunaPolo S, Abu Al-Halawa D, Agha H, Abu Seir R, Hoteit M, Hoteit R, Allehdan S, Behzad N, Bookari K, AlKhalaf M, Al-Sabbah H, Badran E, Tayyem R. Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study. F1000Res 2022; 11:390. [PMID: 36111217 PMCID: PMC9445566 DOI: 10.12688/f1000research.110090.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/16/2022] [Indexed: 02/02/2023] Open
Abstract
Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
- Dpertment of Computer Engineering, Istinye University, Istanbul, 34010, Turkey
| | - Malak Amro
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Stephanny VicunaPolo
- Department of Computer Science, Al- Quds University, Jerusalem, Palestinian Territory
| | - Diala Abu Al-Halawa
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Hazem Agha
- Department of Faculty of Medicine, Al- Quds University, Jerusalem, Palestinian Territory
| | - Rania Abu Seir
- Department of Medical Laboratory Sciences, Al-Quds University, Jerusalem, Palestinian Territory
| | - 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
| | - Reem Hoteit
- Clinical Research Institute, American University of Beirut, Bliss Street, Riad El Solh 1107 2020, Beirut, Lebanon
| | - Sabika Allehdan
- Department of Biology, College of Science, University of Bahrain, Zallaq, Bahrain
| | - Nouf Behzad
- Salmaniya Medical Complex, Ministry of Health, Manama, Bahrain
| | - Khlood Bookari
- Department of Clinical Nutrition, Faculty of Applied Medical Sciences, Taibah University, Medna, Saudi Arabia
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Majid AlKhalaf
- National Nutrition Committee (NNC), Saudi Food and Drug Authority (Saudi FDA), Riyadh, Saudi Arabia
| | - Haleama Al-Sabbah
- Department of Health Sciences, Zayed University, Dubai, United Arab Emirates
| | - Eman Badran
- Faculty of Medicine, University of Jordan, Amman, Jordan
| | - Reema Tayyem
- Department of Human Nutrition, College of Health Sciences, Qatar University, Doha, Qatar
- Department of Nutrition and Food Technology, Faculty of Agriculture, The University of Jordan, Amman, 11942, Jordan
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17
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Craven MP, Fekete EM. Internalized Weight Stigma, Psychological Well-Being, and Sleep in Women. Int J Behav Med 2022; 29:199-208. [PMID: 34173176 DOI: 10.1007/s12529-021-10008-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2021] [Indexed: 11/27/2022]
Abstract
BACKGROUND Many women internalize negative attitudes regarding body shape and size because of the societal standards for women's ideal body shapes and sizes. Internalized weight stigma is related to poorer physical and psychological health. A growing body of research has documented the links between other forms of internalized stigma (e.g., HIV-stigma) and poor sleep quality; however, little research examines the links between internalized weight stigma and sleep or the mechanisms that explain this relationship. Internalized weight stigma may be associated with poor sleep through increases in psychological distress. This study hypothesized that the links between internalized weight stigma and sleep would be mediated by higher levels of depression and anxiety. METHODS About 257 women were recruited via social media, word of mouth, and an undergraduate participant pool. Participants completed an online survey assessing various aspects of weight stigma and health through self-report questionnaires. Most women were non-Hispanic White (86.8%) and had a mean age of 31.40. RESULTS The indirect effects of internalized weight stigma on poorer global sleep quality and daily disturbances through depression and anxiety were significant. In contrast, depressive symptoms but not anxiety explained the links between internalized weight stigma and perceived sleep quality and neither depressive symptoms nor anxiety explained the link between internalized weight stigma and sleep efficiency. CONCLUSIONS Internalized weight stigma is linked to poorer sleep quality, and these links may be explained by psychological well-being. Understanding the mechanisms by which internalized weight stigma is associated with sleep quality can inform the psychological interventions employed.
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Affiliation(s)
- Michael P Craven
- Social and Behavioral Sciences Department, Miami University, Hamilton, OH, 45011, USA.
| | - Erin M Fekete
- School of Psychological Sciences, University of Indianapolis, Indianapolis, IN, USA
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18
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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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19
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Bar-Sella A, Richter T, Zilcha-Mano S, Okon-Singer H. How Social Experiences Affect Interpretation Bias Among Individuals With Non-clinical Depression: The Role of Ostracism. Front Psychiatry 2022; 13:819143. [PMID: 35197878 PMCID: PMC8860160 DOI: 10.3389/fpsyt.2022.819143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 01/03/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Extensive knowledge and research indicate that interpretation bias is very common among individuals with sub-clinical and clinical levels of depression. Nevertheless, little is known about the role of social experiences in enhancing interpretation bias. Given the major relevance of social experiences in the context of depression, the present study investigated the role of potential interactions between social experiences and levels of depression symptoms in the interpretation of ambiguous information. METHOD Seventy participants underwent a laboratory controlled manipulation either of social ostracism or of overinclusion. Participants completed a computerized task that measured both direct and indirect interpretation bias and reported their level of depression symptoms. RESULTS The findings show that ostracism enhanced interpretation bias when symptom levels were higher, while overinclusion did not. This interaction effect between social ostracism and symptom level was found both for direct and for indirect interpretation bias. CONCLUSION Whereas previous research showed the existence of interpretation bias among people with symptoms of depression, the present study expands previous knowledge by shedding light on the conditions under which interpretation bias emerges, suggesting that ostracism enhances negative interpretation of ambiguous information when levels of depression symptoms are higher.
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Affiliation(s)
- Avigail Bar-Sella
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
| | - Thalia Richter
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
| | - Sigal Zilcha-Mano
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel
| | - Hadas Okon-Singer
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
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20
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Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. J Pers Med 2021; 11:jpm11100957. [PMID: 34683098 PMCID: PMC8537335 DOI: 10.3390/jpm11100957] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/12/2021] [Accepted: 09/21/2021] [Indexed: 01/05/2023] Open
Abstract
The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper's overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.
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Affiliation(s)
- Thalia Richter
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa 3498838, Israel; (G.R.-L.); (H.O.-S.)
- Correspondence:
| | - Barak Fishbain
- Faculty of Civil and Environmental Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel;
| | - Gal Richter-Levin
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa 3498838, Israel; (G.R.-L.); (H.O.-S.)
| | - Hadas Okon-Singer
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa 3498838, Israel; (G.R.-L.); (H.O.-S.)
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21
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Richter T, Fishbain B, Fruchter E, Richter-Levin G, Okon-Singer H. Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. J Psychiatr Res 2021; 141:199-205. [PMID: 34246974 DOI: 10.1016/j.jpsychires.2021.06.044] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/20/2021] [Accepted: 06/22/2021] [Indexed: 11/30/2022]
Abstract
In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.
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Affiliation(s)
- Thalia Richter
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel Haifa, Israel.
| | - Barak Fishbain
- Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Eyal Fruchter
- Brus Rappaport Faculty of Medicine, Technion- Israel Institute of Technology, Haifa, Israel
| | - Gal Richter-Levin
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel Haifa, Israel
| | - Hadas Okon-Singer
- Department of Psychology, School of Psychological Sciences, University of Haifa, Mount Carmel Haifa, Israel
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22
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Qasrawi R, Vicuna Polo SP, Abu Al-Halawa D, Hallaq S, Abdeen Z. Schoolchildren’ Depression and Anxiety Risk Factors Assessment and Prediction: Machine Learning Techniques Performance Analysis (Preprint). JMIR Form Res 2021; 6:e32736. [PMID: 35665695 PMCID: PMC9475423 DOI: 10.2196/32736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 02/03/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Background Depression and anxiety symptoms in early childhood have a major effect on children’s mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades. Objective In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren’s depression and anxiety. Methods The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.
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Affiliation(s)
- Radwan Qasrawi
- Department of Computer Science, Al-Quds University, Ramallah, Occupied Palestinian Territory
- Department of Computer Engineering, Istinye University, Istanbul, Turkey
| | - Stephanny Paola Vicuna Polo
- Center for Business Innovation and Technology, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Diala Abu Al-Halawa
- Faculty of Medicine, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Sameh Hallaq
- Al-Quds Bard College for Arts and Sciences, Al-Quds University, Jerusalem, Occupied Palestinian Territory
| | - Ziad Abdeen
- Faculty of Medicine, Al-Quds University, Jerusalem, Occupied Palestinian Territory
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Romaniszyn-Kania P, Pollak A, Bugdol MD, Bugdol MN, Kania D, Mańka A, Danch-Wierzchowska M, Mitas AW. Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods. SENSORS 2021; 21:s21144853. [PMID: 34300591 PMCID: PMC8309702 DOI: 10.3390/s21144853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/11/2021] [Accepted: 07/12/2021] [Indexed: 12/12/2022]
Abstract
Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.
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Affiliation(s)
- Patrycja Romaniszyn-Kania
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
- Correspondence:
| | - Anita Pollak
- Institute of Psychology, University of Silesia in Katowice, Bankowa 12, 40-007 Katowice, Poland;
| | - Marcin D. Bugdol
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
| | - Monika N. Bugdol
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
| | - Damian Kania
- Institute of Physiotherapy and Health Sciences, The Jerzy Kukuczka Academy of Physical Education in Katowice, Mikołowska 72A, 40-065 Katowice, Poland;
| | - Anna Mańka
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
| | - Marta Danch-Wierzchowska
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
| | - Andrzej W. Mitas
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland; (M.D.B.); (M.N.B.); (A.M.); (M.D.-W.); (A.W.M.)
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24
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Liu H, Yuan H, Wang Y, Huang W, Xue H, Zhang X. Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Sci Rep 2021; 11:12868. [PMID: 34145330 PMCID: PMC8213829 DOI: 10.1038/s41598-021-92287-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 05/28/2021] [Indexed: 01/30/2023] Open
Abstract
Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to young-middle-aged inpatients. The aim of this study was to develop and externally validate a new prediction model for young-middle-aged people using machine learning methods. The clinical data sets linked with 167 inpatients with deep venous thrombosis (DVT) and/or pulmonary embolism (PE) and 406 patients without DVT or PE were compared and analysed with machine learning techniques. Five algorithms, including logistic regression, decision tree, feed-forward neural network, support vector machine, and random forest, were used for training and preparing the models. The support vector machine model had the best performance, with AUC values of 0.806-0.944 for 95% CI, 59% sensitivity and 99% specificity, and an accuracy of 87%. Although different top predictors of adverse outcomes appeared in the different models, life-threatening illness, fibrinogen, RBCs, and PT appeared to be more consistently featured by the different models as top predictors of adverse outcomes. Clinical data sets of young and middle-aged inpatients can be used to accurately predict the risk of VTE with a support vector machine model.
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Affiliation(s)
- Hua Liu
- China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130000, People's Republic of China
| | - Hua Yuan
- School of Nursing, Jilin University, Changchun, 130021, Jilin, People's Republic of China
| | - Yongmei Wang
- The Second Hospital of Jilin University, Changchun, 130000, Jilin, People's Republic of China
| | - Weiwei Huang
- China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130000, People's Republic of China
| | - Hui Xue
- Department of Histology & Embryology, College of Basic Medical Sciences, Jilin University, Changchun, 130021, People's Republic of China.
| | - Xiuying Zhang
- School of Nursing, Jilin University, Changchun, 130021, Jilin, People's Republic of China.
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25
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Gozansky E, Moscona G, Okon-Singer H. Identifying Variables That Predict Depression Following the General Lockdown During the COVID-19 Pandemic. Front Psychol 2021; 12:680768. [PMID: 34079505 PMCID: PMC8165248 DOI: 10.3389/fpsyg.2021.680768] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/20/2021] [Indexed: 12/23/2022] Open
Abstract
This study aimed to define the psychological markers for future development of depression symptoms following the lockdown caused by the COVID-19 outbreak. Based on previous studies, we focused on loneliness, intolerance of uncertainty and emotion estimation biases as potential predictors of elevated depression levels. During the general lockdown in April 2020, 551 participants reported their psychological health by means of various online questionnaires and an implicit task. Out of these participants, 129 took part in a second phase in June 2020. Subjective loneliness during the lockdown rather than objective isolation was the strongest predictor of symptoms of depression 5 weeks later. Younger age and health related worry also predicted higher non-clinical levels of depression and emotional distress. The results support the diathesis-stress model, which posits that a combination of preexisting vulnerabilities along with stressors such as negative life events are among the factors affecting the development of psychopathology. Moreover, our results correspond with those of previous studies conducted worldwide during the COVID-19 pandemic. Taken together, these findings call for focusing on psychological factors, especially among younger people, to identify individuals at risk for future development of depression and to promote new strategies for prevention.
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Affiliation(s)
- Einav Gozansky
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
| | - Gal Moscona
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
| | - Hadas Okon-Singer
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, Haifa, Israel
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26
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Jin H, Chien S, Meijer E, Khobragade P, Lee J. Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study. JMIR Ment Health 2021; 8:e27113. [PMID: 33970122 PMCID: PMC8145077 DOI: 10.2196/27113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/11/2021] [Accepted: 04/17/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. OBJECTIVE This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. METHODS Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. RESULTS Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. CONCLUSIONS The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.
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Affiliation(s)
- Haomiao Jin
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Sandy Chien
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Erik Meijer
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
- RAND Corporation, Santa Monica, CA, United States
| | - Pranali Khobragade
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
- RAND Corporation, Santa Monica, CA, United States
- Department of Economics, University of Southern California, Los Angeles, CA, United States
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27
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Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021; 15:595507. [PMID: 33967705 PMCID: PMC8097000 DOI: 10.3389/fnsys.2021.595507] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yueyi Chen
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.,Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Emma Wellmeyer
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Lauren C Smith
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Brianna Aragon Montes
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Adam Kimbrough
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States
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28
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Salzer Y, Honig HH, Shaked R, Abeles E, Kleinjan-Elazary A, Berger K, Jacoby S, Fishbain B, Kendler S. Towards on-site automatic detection of noxious events in dairy cows. Appl Anim Behav Sci 2021. [DOI: 10.1016/j.applanim.2021.105260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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29
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Abado E, Aue T, Okon-Singer H. Cognitive Biases in Blood-Injection-Injury Phobia: A Review. Front Psychiatry 2021; 12:678891. [PMID: 34326784 PMCID: PMC8313757 DOI: 10.3389/fpsyt.2021.678891] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/17/2021] [Indexed: 12/20/2022] Open
Abstract
Blood-injection-injury (BII) phobia can lead to avoidance of crucial medical procedures and to detrimental health consequences, even among health workers. Yet unlike other specific phobias, BII phobia has been understudied. Specifically, while cognitive biases have been extensively investigated in other anxiety disorders, little is known about the same biases in BII phobia. The current article reviews cognitive biases in BII phobia and suggest future directions for further study and treatment. The reviewed biases include attention, expectancy, memory, perception, and interpretation biases. The investigation of these biases is highly relevant, as cognitive biases have been found to interact with anxiety symptoms. Results showed that attention, expectancy, and memory biases are involved in BII phobia, while no studies were found on interpretation nor perception biases. Mixed results were found for attention bias, as different studies found different components of attention bias, while others found no attention bias at all. Similarly, some studies found a-priori/a-posteriori expectancy biases, while other studies found only one type of bias. A better understanding of the cognitive particularities of BII phobia may lead to better treatments and ultimately reduce avoidance of needles and blood-related situations, thereby enabling individuals with BII phobia to undergo potentially life-saving medical procedures.
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Affiliation(s)
- Elinor Abado
- School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center, University of Haifa, Haifa, Israel
| | - Tatjana Aue
- Department of Psychology, University of Bern, Bern, Switzerland
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, Haifa, Israel.,The Integrated Brain and Behavior Research Center, University of Haifa, Haifa, Israel
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30
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Abado E, Aue T, Okon-Singer H. The Missing Pieces of the Puzzle: A Review on the Interactive Nature of A-Priori Expectancies and Attention Bias toward Threat. Brain Sci 2020; 10:E745. [PMID: 33080803 PMCID: PMC7602966 DOI: 10.3390/brainsci10100745] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/13/2020] [Accepted: 10/14/2020] [Indexed: 01/10/2023] Open
Abstract
The role of attention bias in the etiology and maintenance of anxiety disorders has been studied extensively over decades. Attention bias reflects maladaptation in cognitive processing, as perceived threatening stimuli receive prioritized processing even when they are task-irrelevant or factually unthreatening. Recently, there has been some interest in the role of a-priori expectancies in attention bias toward threat. The current review article will present recent studies as examples that emphasize the need for more comprehensive research about the interactive effects of various factors that affect the relationship between expectancies and attention bias toward threatening stimuli in anxiety. The current review article suggests a holistic view, which advocates for more integrative research, as a dynamic network could underlie changes in attention bias. The study of the interaction between such factors, with a focus on expectancy, can lead to more ecological and clinically important results, and thus to more informed and fine-tuned treatments that are based on manipulation of expectancies. Such methods, in turn, can also help in shedding light on the research of attention bias, in a mutual relationship between research and therapy.
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Affiliation(s)
- Elinor Abado
- School of Psychological Sciences, University of Haifa, 3498838 Haifa, Israel;
- The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, 3498838 Haifa, Israel
| | - Tatjana Aue
- Department of Psychology, University of Bern, 3012 Bern, Switzerland;
| | - Hadas Okon-Singer
- School of Psychological Sciences, University of Haifa, 3498838 Haifa, Israel;
- The Integrated Brain and Behavior Research Center (IBBR), University of Haifa, 3498838 Haifa, Israel
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