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Guo X, Wang L, Li Z, Feng Z, Lu L, Jiang L, Zhao L. Factors and pathways of non-suicidal self-injury in children: insights from computational causal analysis. Front Public Health 2024; 12:1305746. [PMID: 38532971 PMCID: PMC10963487 DOI: 10.3389/fpubh.2024.1305746] [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: 10/02/2023] [Accepted: 02/23/2024] [Indexed: 03/28/2024] Open
Abstract
Background Non-suicidal self-injury (NSSI) has become a significant public health issue, especially prevalent among adolescents. The complexity and multifactorial nature of NSSI necessitate a comprehensive understanding of its underlying causal factors. This research leverages the causal discovery methodology to explore these causal associations in children. Methods An observational dataset was scrutinized using the causal discovery method, particularly employing the constraint-based approach. By integrating machine learning and causal inference techniques, the study aimed to determine direct causal relationships associated with NSSI. The robustness of the causal relationships was evaluated using three methods to construct and validate it: the PC (Peter and Clark) method, Fast Causal Inference (FCI) method, and the GAE (Graphical Autoencoder) method. Results Analysis identified nine nodes with direct causal relationships to NSSI, including life satisfaction, depression, family dysfunction, sugary beverage consumption, PYD (positive youth development), internet addiction, COVID-19 related PTSD, academic anxiety, and sleep duration. Four principal causal pathways were identified, highlighting the roles of lockdown-induced lifestyle changes, screen time, positive adolescent development, and family dynamics in influencing NSSI risk. Conclusions An in-depth analysis of the factors leading to Non-Suicidal Self-Injury (NSSI), highlighting the intricate connections among individual, family, and pandemic-related influences. The results, derived from computational causal analysis, underscore the critical need for targeted interventions that tackle these diverse causative factors.
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Affiliation(s)
- Xinyu Guo
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Linna Wang
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Zhenchao Li
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ziliang Feng
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Li Lu
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Lihua Jiang
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
- Teaching and Research Section of General Practice, The General Practice Medical Center, West China Hospital of Sichuan University, Chengdu, China
| | - Li Zhao
- Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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Anderson LM, Lim KO, Kummerfeld E, Crosby RD, Crow SJ, Engel SG, Forrest L, Wonderlich SA, Peterson CB. Causal discovery analysis: A promising tool in advancing precision medicine for eating disorders. Int J Eat Disord 2023; 56:2012-2021. [PMID: 37548100 DOI: 10.1002/eat.24040] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 07/15/2023] [Accepted: 07/26/2023] [Indexed: 08/08/2023]
Abstract
OBJECTIVE Precision medicine (i.e., individually tailored treatments) represents an optimal goal for treating complex psychiatric disorders, including eating disorders. Within the eating disorders field, most treatment development efforts have been limited in their ability to identify individual-level models of eating disorder psychopathology and to develop and apply an individually tailored treatment for a given individual's personalized model of psychopathology. In addition, research is still needed to identify causal relationships within a given individual's model of eating disorder psychopathology. Addressing this limitation of the current state of precision medicine-related research in the field will allow us to progress toward advancing research and practice for eating disorders treatment. METHOD We present a novel set of analytic tools, causal discovery analysis (CDA) methods, which can facilitate increasingly fine-grained, person-specific models of causal relations among cognitive, behavioral, and affective symptoms. RESULTS CDA can advance the identification of an individual's causal model that maintains that individuals' eating disorder psychopathology. DISCUSSION In the current article, we (1) introduce CDA methods as a set of promising analytic tools for developing precision medicine methods for eating disorders including the potential strengths and weaknesses of CDA, (2) provide recommendations for future studies utilizing this approach, and (3) outline the potential clinical implications of using CDA to generate personalized models of eating disorder psychopathology. PUBLIC SIGNIFICANCE STATEMENT CDA provides a novel statistical approach for identifying causal relationships among variables of interest for a given individual. Person-specific causal models may offer a promising approach to individualized treatment planning and inform future personalized treatment development efforts for eating disorders.
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Affiliation(s)
- Lisa M Anderson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ross D Crosby
- Center for Biobehavioral Research, Sanford Research, Fargo, North Dakota, USA
| | - Scott J Crow
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
- Accanto Health, St Paul, Minnesota, USA
| | - Scott G Engel
- Center for Biobehavioral Research, Sanford Research, Fargo, North Dakota, USA
| | - Lauren Forrest
- Department of Psychiatry and Behavioral Health, Penn State Hershey Medical Center, Hershey, Pennsylvania, USA
| | | | - Carol B Peterson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
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Lyu K, Tian Y, Shang Y, Zhou T, Yang Z, Liu Q, Yao X, Zhang P, Chen J, Li J. Causal knowledge graph construction and evaluation for clinical decision support of diabetic nephropathy. J Biomed Inform 2023; 139:104298. [PMID: 36731730 DOI: 10.1016/j.jbi.2023.104298] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/25/2022] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND Many important clinical decisions require causal knowledge (CK) to take action. Although many causal knowledge bases for medicine have been constructed, a comprehensive evaluation based on real-world data and methods for handling potential knowledge noise are still lacking. OBJECTIVE The objectives of our study are threefold: (1) propose a framework for the construction of a large-scale and high-quality causal knowledge graph (CKG); (2) design the methods for knowledge noise reduction to improve the quality of the CKG; (3) evaluate the knowledge completeness and accuracy of the CKG using real-world data. MATERIAL AND METHODS We extracted causal triples from three knowledge sources (SemMedDB, UpToDate and Churchill's Pocketbook of Differential Diagnosis) based on rule methods and language models, performed ontological encoding, and then designed semantic modeling between electronic health record (EHR) data and the CKG to complete knowledge instantiation. We proposed two graph pruning strategies (co-occurrence ratio and causality ratio) to reduce the potential noise introduced by SemMedDB. Finally, the evaluation was carried out by taking the diagnostic decision support (DDS) of diabetic nephropathy (DN) as a real-world case. The data originated from a Chinese hospital EHR system from October 2010 to October 2020. The knowledge completeness and accuracy of the CKG were evaluated based on three state-of-the-art embedding methods (R-GCN, MHGRN and MedPath), the annotated clinical text and the expert review, respectively. RESULTS This graph included 153,289 concepts and 1,719,968 causal triples. A total of 1427 inpatient data were used for evaluation. Better results were achieved by combining three knowledge sources than using only SemMedDB (three models: area under the receiver operating characteristic curve (AUC): p < 0.01, F1: p < 0.01), and the graph covered 93.9 % of the causal relations between diseases and diagnostic evidence recorded in clinical text. Causal relations played a vital role in all relations related to disease progression for DDS of DN (three models: AUC: p > 0.05, F1: p > 0.05), and after pruning, the knowledge accuracy of the CKG was significantly improved (three models: AUC: p < 0.01, F1: p < 0.01; expert review: average accuracy: + 5.5 %). CONCLUSIONS The results demonstrated that our proposed CKG could completely and accurately capture the abstract CK under the concrete EHR data, and the pruning strategies could improve the knowledge accuracy of our CKG. The CKG has the potential to be applied to the DDS of diseases.
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Affiliation(s)
- Kewei Lyu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yong Shang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Ziyue Yang
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Qianghua Liu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Xi Yao
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ping Zhang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
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Saxe GN, Bickman L, Ma S, Aliferis C. Mental health progress requires causal diagnostic nosology and scalable causal discovery. Front Psychiatry 2022; 13:898789. [PMID: 36458123 PMCID: PMC9705733 DOI: 10.3389/fpsyt.2022.898789] [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: 03/17/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.
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Affiliation(s)
- Glenn N. Saxe
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Leonard Bickman
- Ontrak Health, Inc., Henderson, NV, United States
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Sisi Ma
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Constantin Aliferis
- Program in Data Science, Department of Medicine, Clinical and Translational Science Institute, Institute for Health Informatics, School of Medicine, University of Minnesota, Minneapolis, MN, United States
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Fitzgerald JM, Webb EK, Weis CN, Huggins AA, Bennett KP, Miskovich TA, Krukowski JL, deRoon-Cassini TA, Larson CL. Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:139-149. [PMID: 34478884 PMCID: PMC8825698 DOI: 10.1016/j.bpsc.2021.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/18/2021] [Accepted: 08/22/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest-based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms. METHODS A total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values). RESULTS Acute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003). CONCLUSIONS Findings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.
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Affiliation(s)
| | - Elisabeth Kate Webb
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Carissa N. Weis
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
| | - Ashley A. Huggins
- Medical University of South Carolina, Department of Psychiatry, Charleston, SC, USA
| | | | | | | | - Terri A. deRoon-Cassini
- Medical College of Wisconsin, Department of Surgery, Division of Trauma & Acute Care Surgery, Milwaukee, WI, USA
| | - Christine L. Larson
- University of Wisconsin-Milwaukee, Department of Psychology, Milwaukee, WI, USA
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Musa GJ, Geronazzo-Alman L, Fan B, Cheslack-Postava K, Bavley R, Wicks J, Bresnahan M, Amsel L, Fiano E, Saxe G, Kummerfeld E, Ma S, Hoven CW. Neighborhood characteristics and psychiatric disorders in the aftermath of mass trauma: A representative study of New York City public school 4th-12th graders after 9/11. J Psychiatr Res 2021; 138:584-590. [PMID: 33992981 DOI: 10.1016/j.jpsychires.2021.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/28/2021] [Accepted: 05/01/2021] [Indexed: 11/29/2022]
Abstract
Studies of the relationship between neighborhood characteristics and childhood/adolescent psychopathology in large samples examined one outcome only, and/or general (e.g., 'psychological distress') or aggregate (e.g., 'any anxiety disorder') measures of psychopathology. Thus, in the only representative sample of New York City public school 4th-12th graders (N = 8202) surveyed after the attacks of 9/11/2001, this study examined whether (1) indices of neighborhood Socioeconomic Status, Quality, and Safety and (2) neighborhood disadvantage (defined as multidimensional combinations of SES, Quality and Safety indicators) are associated with eight psychiatric disorders: posttraumatic stress disorder, separation anxiety disorder (SAD), agoraphobia, generalized anxiety disorder (GAD), panic disorder, major depression, conduct disorder, and alcohol use disorder (AUD). (1) The odds ratios (OR) of psychiatric disorders were between 0.55 (AUD) and 1.55 (agoraphobia), in low and intermediate-low SES neighborhoods, respectively, between 0.50 (AUD) and 2.54 (agoraphobia) in low Quality neighborhoods, and between 0.52 (agoraphobia) and 0.65 (SAD) in low Safety neighborhoods. (2) In neighborhoods characterized by high disadvantage, the OR were between 0.42 (AUD) and 1.36 (SAD). This study suggests that neighborhood factors are important social determinants of childhood/adolescent psychopathology, even in the aftermath of mass trauma. At the community level, interventions on modifiable neighborhood characteristics and targeted resources allocation to high-risk contexts could have a cost-effective broad impact on children's mental health. At the individual-level, increased knowledge of the living environment during psychiatric assessment and treatment could improve mental health outcomes; for example, specific questions about neighborhood factors could be incorporated in DSM-5's Cultural Formulation Interview.
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Affiliation(s)
- George J Musa
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Lupo Geronazzo-Alman
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA.
| | - Bin Fan
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Keely Cheslack-Postava
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Rachel Bavley
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Judith Wicks
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Michaeline Bresnahan
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Lawrence Amsel
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Emily Fiano
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
| | - Glenn Saxe
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Erich Kummerfeld
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Sisi Ma
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Christina W Hoven
- New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, NY, USA
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