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Ding H, Li N, Li L, Xu Z, Xia W. Machine learning-enabled mental health risk prediction for youths with stressful life events: A modelling study. J Affect Disord 2024; 368:537-546. [PMID: 39306010 DOI: 10.1016/j.jad.2024.09.111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 09/10/2024] [Accepted: 09/15/2024] [Indexed: 09/25/2024]
Abstract
BACKGROUND Youths face significant mental health challenges exacerbated by stressful life events, particularly in the context of the COVID-19 pandemic. Immature coping strategies can worsen mental health outcomes. METHODS This study utilised a two-wave cross-sectional survey design with data collected from Chinese youth aged 14-25 years. Wave 1 (N = 3038) and Wave 2 (N = 539) datasets were used for model development and external validation, respectively. Twenty-five features, encompassing dimensions related to demographic information, stressful life events, social support, coping strategies, and emotional intelligence, were input into the model to predict the mental health status of youth, which was considered their coping outcome. Shapley additive explanation (SHAP) was used to determine the importance of each risk factor in the feature selection. The intersection of top 10 features identified by random forest and XGBoost were considered the most influential predictors of mental health during the feature selection process, and was then taken as the final set of features for model development. Machine learning models, including logistic regression, AdaBoost, and a backpropagation neural network (BPNN), were trained to predict the outcomes. The optimum model was selected according to the performance in both internal and external validation. RESULTS This study identified six key features that were significantly associated with mental health outcomes: punishment, adaptation issues, self-regulation of emotions, learning pressure, use of social support, and recognition of others' emotions. The BPNN model, optimized through feature selection methods like SHAP, demonstrated superior performance in internal validation (C-index [95 % CI] = 0.9120 [0.9111, 0.9129], F-score [95 % CI] = 0.8861 [0.8853, 0.8869]). Additionally, external validation showed the model had strong discrimination (C-index = 0.9749, F-score = 0.8442) and calibration (Brier score = 0.029) capabilities. LIMITATIONS Although the clinical prediction model performed well, the study it still limited by self-reported data and representativeness of samples. Causal relationships need to be established to interpret the coping mechanism from multiple perspectives. Also, the limited data on minority groups may lead to algorithmic unfairness. CONCLUSIONS Machine learning models effectively identified and predicted mental health outcomes among youths, with the SHAP+BPNN model showing promising clinical applicability. These findings emphasise the importance and effectiveness of targeted interventions with the help of clinical prediction model.
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Affiliation(s)
- Hexiao Ding
- School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
| | - Na Li
- School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China.
| | - Lishan Li
- School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China.
| | - Ziruo Xu
- School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China.
| | - Wei Xia
- School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China.
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Sidi A, Kiio M, Mwangi N, Olum M, Githinji G, Maina K, Kithinji C, Kaloki F, Temmerman M, Manguro G. Prevalence and correlates of depressive disorders in commercially sexual exploited children: A cross-sectional study in Mombasa, Kenya. CHILD ABUSE & NEGLECT 2024; 149:106690. [PMID: 38354599 DOI: 10.1016/j.chiabu.2024.106690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Commercial sexual exploitation of children (CSEC) is a global concern and is among the common forms of sexual violence against children. In Kenya, about 32 % of girls and 16 % of boys experience sexual violence before the age of 18 years. While much has been written about the impact of child sexual exploitation, there's little on the prevalence of depressive disorders among CSE children. OBJECTIVES This study was conducted to assess the prevalence and correlates of depressive disorders among CSE children in Mombasa, Kenya. PARTICIPANTS AND SETTING The study was conducted among CSE children (10-17 years) in Mombasa County. METHODS A cross-sectional study conducted between May 2021 and June 2022. A total of 409 CSE children were enrolled, using a case management approach. Data was collected using the child identification tool and the Patient Health Questionnaire (PHQ-9), at the first counselling session to determine the prevalence of depression levels. RESULTS Of the 409 children, 367 (90 %) were girls while 42 (10 %) were boys. The mean age was 15 years (10-17) (SD = 1.4, t = 0.765). In 286 (70 %) 'Depression unlikely' was recorded, while 123 (30 %) 'Depression likelihood.' Prevalence of mild to severe depression was significantly higher in girls than boys (p = 0.002). Other determinants of depression were having a primary level of education (p = 0.03) and being an orphan. (p = 0.03). CONCLUSION It is important to prioritize mental health interventions such as screening and early diagnosis of mental health among CSE children in order to prevent and manage both short and long term effects.
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Affiliation(s)
- Abigael Sidi
- International Centre for Reproductive Health, Kenya.
| | - Morris Kiio
- International Centre for Reproductive Health, Kenya
| | - Norah Mwangi
- International Centre for Reproductive Health, Kenya
| | - Melanie Olum
- International Centre for Reproductive Health, Kenya
| | | | | | | | | | - Marleen Temmerman
- International Centre for Reproductive Health, Kenya; Aga Khan University, Nairobi, Kenya
| | - Griffins Manguro
- International Centre for Reproductive Health, Kenya; Ghent University, Faculty of Health Sciences, Belgium
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Wang J, Ouyang H, Jiao R, Zhang H, Cheng S, Shang Z, Jia Y, Yan W, Wu L, Liu W. Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis. Digit Health 2024; 10:20552076241239238. [PMID: 38495863 PMCID: PMC10943756 DOI: 10.1177/20552076241239238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction Recent years have witnessed a persistent threat to public mental health, especially during and after the COVID-19 pandemic. Posttraumatic stress disorder (PTSD) has emerged as a pivotal concern amidst this backdrop. Concurrently, machine learning (ML) techniques have progressively applied in the realm of mental health. Therefore, our present undertaking seeks to provide a comprehensive assessment of studies employing ML methods that use diverse data modalities on the classification of people with PTSD. Methods and analysis In pursuit of pertinent studies, we will search both English and Chinese databases from January 2000 to May 2022. Two researchers will independently conduct screening, extract data and assess study quality. We intend to employ the assessment framework introduced by Luis Francisco Ramos-Lima in 2020 for quality evaluation. Rate, standard error and 95% CIs will be utilized for effect size measurement. A Cochran's Q test will be applied to assess heterogeneity. Subgroup and sensitivity analysis will further elucidate the source of heterogeneity and funnel plots and Egger's test will detect publication bias. Ethics and dissemination This systematic review and meta-analysis does not encompass patient interactions or engagements with healthcare providers. The outcomes of this research will be disseminated through scholarly channels, including presentations at scientific conferences and publications in peer-reviewed journals.PROSPERO registration number CRD42023342042.
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Affiliation(s)
- Jing Wang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Hui Ouyang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Runda Jiao
- Graduate School, PLA General Hospital, Beijing, China
| | - Haiyan Zhang
- Department of Health Care, The First Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Suhui Cheng
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Zhilei Shang
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Yanpu Jia
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Wenjie Yan
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Lili Wu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
| | - Weizhi Liu
- Lab for Post-traumatic Stress Disorder, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
- The Emotion & Cognition Lab, Faculty of Psychology and Mental Health, Naval Medical University, Shanghai, China
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Guo J, Chen S, Li F, Hou S, Guo M, Yuan X. CXCL8 delivered by plasma-derived exosomes induces the symptoms of post-traumatic stress disorder through facilitating astrocyte-neuron communication. J Psychiatr Res 2023; 161:261-272. [PMID: 36947957 DOI: 10.1016/j.jpsychires.2023.02.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/01/2023] [Accepted: 02/28/2023] [Indexed: 03/24/2023]
Abstract
Extracellular vesicles (EVs) play an important role in post-traumatic stress disorder (PTSD). This study is aimed to investigate the possible molecular mechanism of CD63 mediating CXCL8 delivery via EVs to affect astrocyte-neuron communication in PTSD. The neuron-derived EVs (NDEVs) and astrocyte-derived EVs (ADEVs) were isolated from plasma in PTSD patients. Next, the uptake of EVs by neurons was assessed. Following determination of the interaction between CD63 and CXCL8, gain- and loss-of-function experiments were performed in astrocytes. Finally, a PTSD mouse model was established using the single prolonged stress and electric foot shock to confirm the effects of plasma-derived EVs delivering CXCL8 on anxiety- and depression-like behaviors in PTSD mice. EVs derived from plasma of PTSD patients aggravated anxiety- and depression-like behaviors in PTSD mice. CXCL8 was a key gene upregulated in both NDEVs and ADEVs from plasma of PTSD patients, which could be delivered into EVs by CD63. Meanwhile, CXCL8 was also highly expressed in plasma-derived EVs. In vivo experiments also verified that plasma-derived EVs could enhance astrocyte-neuron communication by delivering CXCL8, and silencing of CXCL8 ameliorated anxiety- and depression-like behaviors in PTSD mice. Taken together, CD63 promotes delivery of CXCL8 via EVs to induce PTSD by enhancing astrocyte-neuron communication, suggesting the potential of CD63 mediating delivery of CXCL8 via EVs as a therapeutic target for PTSD.
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Affiliation(s)
- Juncheng Guo
- Scientific Research Department, Haikou People's Hospital, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, 570208, PR China
| | - Siran Chen
- Department of Humanities and Social Sciences, Hainan Medical University, Haikou, 571199, PR China
| | - Feiyan Li
- Psychology Department, Hainan General Hospital, Haikou, 570311, PR China
| | - Shiyi Hou
- Psychology Department, Haikou People's Hospital, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, 570208, PR China
| | - Min Guo
- Scientific Research Department, Hainan General Hospital, Haikou, 570311, PR China
| | - Xiuhong Yuan
- Haikou People's Hospital, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, 570208, PR China; Department of Clinical Psychology, The Third Xiangya Hospital of Central South University, Changsha, 410013, PR China.
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Iqbal T, Elahi A, Wijns W, Amin B, Shahzad A. Improved Stress Classification Using Automatic Feature Selection from Heart Rate and Respiratory Rate Time Signals. APPLIED SCIENCES 2023; 13:2950. [DOI: 10.3390/app13052950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature’s 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification.
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Affiliation(s)
- Talha Iqbal
- Smart Sensor Lab, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Adnan Elahi
- Electrical and Electronic Engineering, University of Galway, H91 TK33 Galway, Ireland
| | - William Wijns
- Smart Sensor Lab, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- CÚRAM Center for Research in Medical Devices, H91 W2TY Galway, Ireland
| | - Bilal Amin
- Smart Sensor Lab, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Electrical and Electronic Engineering, University of Galway, H91 TK33 Galway, Ireland
| | - Atif Shahzad
- Smart Sensor Lab, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland
- Centre for Systems Modelling and Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham B15 2TT, UK
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A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare (Basel) 2023; 11:healthcare11030285. [PMID: 36766860 PMCID: PMC9914523 DOI: 10.3390/healthcare11030285] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.
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7
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Luo L. The practice of psychological well-being education model for poor university students from the perspective of positive psychology. Front Psychol 2022; 13:951668. [PMID: 35978785 PMCID: PMC9376323 DOI: 10.3389/fpsyg.2022.951668] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Poor university students are a special group. Social development provides many positive factors for poor university students' personality and psychological development, but negative factors are also accompanied by them, which affect the psychological health of poor university students. University students are in a period of rapid physical and mental development, and it is an important issue that colleges and universities need to solve psychological well-being education. We hope to find out the aspects that can be studied in the irregularity of various factors that affect college students' mental health. BP neural network is a typical model of artificial neural network. Based on the BP algorithm and the fuzzy comprehensive evaluation of the psychological well-being prediction system for poor university students, this paper systematically summarizes the concept of psychological well-being, the factors that affect psychological well-being, and the related research done by predecessors on psychological well-being. Using the international psychological well-being scale SCL-90 to comprehensively consider the psychological well-being status of poor university students and select the optimized BP algorithm to establish a psychological well-being prediction model, and implement it and compare it with other models to reflect its superiority. Data were collected and analyzed by means of a questionnaire. The regression model was used to analyze the relationship between mindfulness, rumination and psychological well-being. The mediation index fitted by the model reached 0.9. The model can reflect the real situation of the data, that is, rumination plays a partial mediating role in the effect of mindfulness on psychological well-being. The introduction of this psychological prediction model into the psychological well-being education of poor university students not only helps to improve the educational concept and expand the educational approach, but also helps to achieve the goal of psychological well-being education for poor university students, thereby promoting the improvement of the psychological quality of poor university students.
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Affiliation(s)
- Ling Luo
- Students’ Affairs Department, Henan Polytechnic University, Jiaozuo, China
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8
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Xin Y, Ren X. Predicting depression among rural and urban disabled elderly in China using a random forest classifier. BMC Psychiatry 2022; 22:118. [PMID: 35168579 PMCID: PMC8845343 DOI: 10.1186/s12888-022-03742-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 01/27/2022] [Indexed: 12/18/2022] Open
Abstract
With global aging, the number of elderly with physical disabilities is also increasing. Compared with the ordinary elderly, the elderly who lose their independence are more likely to have the symptoms of depression. Reducing depression may help to alleviate the disability process of those who find themselves in the disabled stages. Therefore, the purpose of this study is to explore the predictive effects of demographic characteristics, health behavior, health status, family relations, social relations, and subjective attitude on depression in rural and urban disabled elderly to improve early depression symptom recognition.A total of 1460 older adults aged 60 and disabled were selected from China Family Panel Studies (CFPS). Depression was assessed according to The Center for Epidemiologic Studies Depression Scale (CES-D). This paper used the random forest classifier to predict the depression of the disabled elderly from six aspects: demographic characteristics, health status, health behavior, family relationship, and social relationship. The prediction model was established based on 70% of the training set and 30% of the test set. The depression rate of rural disabled elderly was 57.67%, and that of urban disabled elderly was 44.59%. The mean values of the 10-k cross-validated results were 0.71 in rural areas and 0.70 in urban areas. AUC:0.71, specificity: 65.3%, sensitivity: 80.6% for rural disabled elderly with depression; AUC:0.78, specificity: 78.1%, sensitivity: 64.2% for urban disabled elderly with depression, respectively. There are apparent differences in the top ten predictors between rural and urban disabled elderly. The common predictors were self-rated health, changing in perceived health, disease or accidence experience within the past 2 weeks, life satisfaction, trusting people, BMI, and having trust in the future. Non-common predictors were chronic diseases, neighborly relations, total medical expenses within 1 year, community emotion, sleep duration, and family per capita income. Using random forest data to predict the depression of the disabled elderly may lead to early detection of depression.
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Affiliation(s)
- Yu Xin
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaohui Ren
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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9
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Chen N, Wang HB, Wu BQ, Jiang JH, Yang JT, Tang LJ, He HQ, Linghu DD. Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC-MS urinary metabolomics. Talanta 2021; 235:122720. [PMID: 34517588 DOI: 10.1016/j.talanta.2021.122720] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023]
Abstract
Inborn errors of metabolism, also known as inherited metabolic diseases (IMDs), are related to genetic mutations and cause corresponding biochemical metabolic disorder of newborns and even sudden infant death. Timely detection and diagnosis of IMDs are of great significance for improving survival of newborns. Here we propose a strategy for simultaneously detecting six types of IMDs via combining GC-MS technique with the random forest algorithm (RF). Clinical urine samples from IMD and healthy patients are analyzed using GC-MS for acquiring metabolomics data. Then, the RF model is established as a multi-classification tool for the GC-MS data. Compared with the models built by artificial neural network and support vector machine, the results demonstrated the RF model has superior performance of high specificity, sensitivity, precision, accuracy, and matthews correlation coefficients on identifying all six types of IMDs and normal samples. The proposed strategy can afford a useful method for reliable and effective identification of multiple IMDs in clinical diagnosis.
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Affiliation(s)
- Nan Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China
| | - Hai-Bo Wang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China
| | - Ben-Qing Wu
- Department of Pediatric, University of Chinese Academy of Sciences-Shenzhen Hospital, Shenzhen, 518000, PR China
| | - Jian-Hui Jiang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China.
| | - Jiang-Tao Yang
- Shenzhen Aone Medical Laboratory Co, Ltd, Shenzhen, 518000, PR China
| | - Li-Juan Tang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China.
| | - Hong-Qin He
- Yuncheng Maternal and Child Health Hospital, Yuncheng, Shanxi, 044000, PR China
| | - Dan-Dan Linghu
- Yuncheng Maternal and Child Health Hospital, Yuncheng, Shanxi, 044000, PR China
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10
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Bhattarai A, Dimitropoulos G, Marriott B, Paget J, Bulloch AGM, Tough SC, Patten SB. Can the adverse childhood experiences (ACEs) checklist be utilized to predict emergency department visits among children and adolescents? BMC Med Res Methodol 2021; 21:195. [PMID: 34563122 PMCID: PMC8465692 DOI: 10.1186/s12874-021-01392-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 09/04/2021] [Indexed: 11/29/2022] Open
Abstract
Background Extensive literature has shown an association of Adverse Childhood Experiences (ACEs) with adverse health outcomes; however, its ability to predict events or stratify risks is less known. Individuals with mental illness and ACE exposure have been shown to visit emergency departments (ED) more often than those in the general population. This study thus examined the ability of the ACEs checklist to predict ED visits within the subsequent year among children and adolescents presenting to mental health clinics with pre-existing mental health issues. Methods The study analyzed linked data (n = 6100) from two databases provided by Alberta Health Services (AHS). The Regional Access and Intake System (RAIS 2016–2018) database provided data on the predictors (ACE items, age, sex, residence, mental health program type, and primary diagnosis) regarding children and adolescents (aged 0–17 years) accessing addiction and mental health services within Calgary Zone, and the National Ambulatory Care Reporting System (NACRS 2016–2019) database provided data on ED visits. A 25% random sample of the data was reserved for validation purposes. Two Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression models, each employing a different method to tune the shrinkage parameter lambda (namely cross-validated and adaptive) and performing 10-fold cross-validation for a set of 100 lambdas in each model were examined. Results The adaptive LASSO model had a slightly better fit in the validation dataset than the cross-validated model; however, it still demonstrated poor discrimination (AUC 0.60, sensitivity 37.8%, PPV 49.6%) and poor calibration (over-triaged in low-risk and under-triaged in high-risk subgroups). The model’s poor performance was evident from an out-of-sample deviance ratio of − 0.044. Conclusion The ACEs checklist did not perform well in predicting ED visits among children and adolescents with existing mental health concerns. The diverse causes of ED visits may have hindered accurate predictions, requiring more advanced statistical procedures. Future studies exploring other machine learning approaches and including a more extensive set of childhood adversities and other important predictors may produce better predictions. Furthermore, despite highly significant associations being observed, ACEs may not be deterministic in predicting health-related events at the individual level, such as general ED use. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-021-01392-w.
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Affiliation(s)
- Asmita Bhattarai
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada. .,Mathison Centre for Research & Education, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.
| | - Gina Dimitropoulos
- Mathison Centre for Research & Education, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Faculty of Social Work, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
| | - Brian Marriott
- Faculty of Social Work, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada.,Addiction and Mental Health, Alberta Health Services- Calgary Zone, Calgary, AB, Canada
| | - Jaime Paget
- Addiction and Mental Health, Alberta Health Services- Calgary Zone, Calgary, AB, Canada
| | - Andrew G M Bulloch
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Mathison Centre for Research & Education, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
| | - Suzanne C Tough
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Department of Pediatrics, Cumming School of Medicine, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
| | - Scott B Patten
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Mathison Centre for Research & Education, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, T2N4Z6, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
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