1
|
Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [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: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
Collapse
Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
| |
Collapse
|
2
|
Li R, Xu Z, Xu J, Pan X, Wu H, Huang X, Feng M. Predicting intubation for intensive care units patients: A deep learning approach to improve patient management. Int J Med Inform 2024; 186:105425. [PMID: 38554589 DOI: 10.1016/j.ijmedinf.2024.105425] [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: 10/24/2023] [Revised: 01/19/2024] [Accepted: 03/20/2024] [Indexed: 04/01/2024]
Abstract
OBJECTIVE For patients in the Intensive Care Unit (ICU), the timing of intubation has a significant association with patients' outcomes. However, accurate prediction of the timing of intubation remains an unsolved challenge due to the noisy, sparse, heterogeneous, and unbalanced nature of ICU data. In this study, our objective is to develop a workflow for pre-processing ICU data and to develop a customized deep learning model to predict the need for intubation. METHODS To improve the prediction accuracy, we transform the intubation prediction task into a time series classification task. We carefully design a sequence of data pre-processing steps to handle the multimodal noisy data. Firstly, we discretize the sequential data and address missing data using interpolation. Next, we employ a sampling strategy to address data imbalance and standardize the data to facilitate faster model convergence. Furthermore, we employ the feature selection technique and propose an ensemble model to combine features learned by different deep learning models. RESULTS The performance is evaluated on Medical Information Mart for Intensive Care (MIMIC)-III, an ICU dataset. Our proposed Deep Feature Fusion method achieves an area under the curve (AUC) of the receiver operating curve (ROC) of 0.8953, surpassing the performance of other deep learning and traditional machine learning models. CONCLUSION Our proposed Deep Feature Fusion method proves to be a viable approach for predicting intubation and outperforms other deep learning and classical machine learning models. The study confirms that high-frequency time-varying indicators, particularly Mean Blood Pressure (MeanBP) and peripheral oxygen saturation (SpO2), are significant risk factors for predicting intubation.
Collapse
Affiliation(s)
- Ruixi Li
- Harbin Institute of Technology Shenzhen, Shenzhen, China.
| | - Zenglin Xu
- Harbin Institute of Technology Shenzhen, Shenzhen, China; Peng Cheng Lab, Shenzhen, China.
| | - Jing Xu
- Harbin Institute of Technology Shenzhen, Shenzhen, China.
| | - Xinglin Pan
- Hong Kong Baptist University, Hong Kong, China.
| | - Hong Wu
- University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaobo Huang
- Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China.
| | - Mengling Feng
- Saw Swee Hock School of Public Health and Institute of Data Science, National University of Singapore, Singapore.
| |
Collapse
|
3
|
Tan M, Xiao Y, Jing F, Xie Y, Lu S, Xiang M, Ren H. Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol. Front Psychiatry 2024; 15:1352420. [PMID: 38287940 PMCID: PMC10822920 DOI: 10.3389/fpsyt.2024.1352420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/02/2024] [Indexed: 01/31/2024] Open
Abstract
Background Mental illnesses represent a significant global health challenge, affecting millions with far-reaching social and economic impacts. Traditional exercise prescriptions for mental health often adopt a one-size-fits-all approach, which overlooks individual variations in mental and physical health. Recent advancements in artificial intelligence (AI) offer an opportunity to tailor these interventions more effectively. Objective This study aims to develop and evaluate a multimodal data-driven AI system for personalized exercise prescriptions, targeting individuals with mental illnesses. By leveraging AI, the study seeks to overcome the limitations of conventional exercise regimens and improve adherence and mental health outcomes. Methods The study is conducted in two phases. Initially, 1,000 participants will be recruited for AI model training and testing, with 800 forming the training set, augmented by 9,200 simulated samples generated by ChatGPT, and 200 as the testing set. Data annotation will be performed by experienced physicians from the Department of Mental Health at Guangdong Second Provincial General Hospital. Subsequently, a randomized controlled trial (RCT) with 40 participants will be conducted to compare the AI-driven exercise prescriptions against standard care. Assessments will be scheduled at 6, 12, and 18 months to evaluate cognitive, physical, and psychological outcomes. Expected outcomes The AI-driven system is expected to demonstrate greater effectiveness in improving mental health outcomes compared to standard exercise prescriptions. Personalized exercise regimens, informed by comprehensive data analysis, are anticipated to enhance participant adherence and overall mental well-being. These outcomes could signify a paradigm shift in exercise prescription for mental health, paving the way for more personalized and effective treatment modalities. Registration and ethical approval This is approved by Human Experimental Ethics Inspection of Guangzhou Sport University, and the registration is under review by ChiCTR.
Collapse
Affiliation(s)
| | - Yanning Xiao
- China Swimming College, Beijing Sport University, Beijing, China
- China’s National Artistic Swimming Team, Beijing, China
- Institute of Physical Education, Sichuan University, Chengdu, China
| | - Fengshi Jing
- Faculty of Data Science, City University of Macau, Taipa, Macao SAR, China
- Project-China, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States
- College of Business, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yewei Xie
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
| | - Sanmei Lu
- South China Agricultural University, Guangzhou, China
| | | | - Hao Ren
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
| |
Collapse
|
4
|
Gao T, Ren H, He S, Liang D, Xu Y, Chen K, Wang Y, Zhu Y, Dong H, Xu Z, Chen W, Cheng W, Jing F, Tao X. Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol. Front Cardiovasc Med 2023; 9:1091885. [PMID: 38106819 PMCID: PMC10722170 DOI: 10.3389/fcvm.2022.1091885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/12/2022] [Indexed: 12/19/2023] Open
Abstract
Background Cardiovascular disease (CVD) and cancer are the first and second causes of death in over 130 countries across the world. They are also among the top three causes in almost 180 countries worldwide. Cardiovascular complications are often noticed in cancer patients, with nearly 20% exhibiting cardiovascular comorbidities. Physical exercise may be helpful for cancer survivors and people living with cancer (PLWC), as it prevents relapses, CVD, and cardiotoxicity. Therefore, it is beneficial to recommend exercise as part of cardio-oncology preventive care. Objective With the progress of deep learning algorithms and the improvement of big data processing techniques, artificial intelligence (AI) has gradually become popular in the fields of medicine and healthcare. In the context of the shortage of medical resources in China, it is of great significance to adopt AI and machine learning methods for prescription recommendations. This study aims to develop an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care, and this paper presents the study protocol. Methods This will be a retrospective machine learning modeling cohort study with interventional methods (i.e., exercise prescription). We will recruit PLWC participants at baseline (from 1 January 2025 to 31 December 2026) and follow up over several years (from 1 January 2027 to 31 December 2028). Specifically, participants will be eligible if they are (1) PLWC in Stage I or cancer survivors from Stage I; (2) aged between 18 and 55 years; (3) interested in physical exercise for rehabilitation; (4) willing to wear smart sensors/watches; (5) assessed by doctors as suitable for exercise interventions. At baseline, clinical exercise physiologist certificated by the joint training program (from 1 January 2023 to 31 December 2024) of American College of Sports Medicine and Chinese Association of Sports Medicine will recommend exercise prescription to each participant. During the follow-up, effective exercise prescription will be determined by assessing the CVD status of the participants. Expected outcomes This study aims to develop not only an interpretable machine learning model to recommend exercise prescription but also an intelligent system of exercise prescription for precision cardio-oncology preventive care. Ethics This study is approved by Human Experimental Ethics Inspection of Guangzhou Sport University. Clinical trial registration http://www.chictr.org.cn, identifier ChiCTR2300077887.
Collapse
Affiliation(s)
- Tianyu Gao
- School of Physical Education, Jinan University, Guangzhou, China
| | - Hao Ren
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Faculty of Data Science, City University of Macau, Macao, Macao SAR, China
| | - Shan He
- Guangzhou Sport University, Guangzhou, China
| | - Deyi Liang
- Guangdong Women and Children Hospital, Guangzhou, China
| | - Yuming Xu
- Division of Physical Education, Guangdong University of Finance and Economics, Guangzhou, China
- School of Education, City University of Macau, Macao, Macao SAR, China
| | - Kecheng Chen
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yufan Wang
- Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuxin Zhu
- Syns Institute of Educational Research, Hong Kong, Hong Kong SAR, China
| | - Heling Dong
- School of Physical Education, Jinan University, Guangzhou, China
| | - Zhongzhi Xu
- School of Public Health, Sun Yat-Sen University, Guangzhou, China
| | - Weiming Chen
- Department of Health Medicine, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Weibin Cheng
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengshi Jing
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Faculty of Data Science, City University of Macau, Macao, Macao SAR, China
- UNC Project-China, UNC Global, School of Medicine, The University of North Carolina, Chapel Hill, NC, United States
| | - Xiaoyu Tao
- Zhuhai College of Science and Technology, Zhuhai, China
- ZCST Health and Medicine Industry Research Institute, Zhuhai, China
| |
Collapse
|
5
|
Lei D, Sun J, Xia J. Cuproptosis-related genes prediction feature and immune microenvironment in major depressive disorder. Heliyon 2023; 9:e18497. [PMID: 37576193 PMCID: PMC10415818 DOI: 10.1016/j.heliyon.2023.e18497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/15/2023] Open
Abstract
Background Major depressive disorder (MDD) is a severe, unpredictable, ill-cured, relapsing neuropsychiatric disorder. A recently identified type of death called cuproptosis has been linked to a number of illnesses. However, the influence of cuproptosis-related genes in MDD has not been comprehensively assessed in prior study. Aim This investigation intends to shed light on the predictive value of cuproptosis-related genes for MDD and the immunological microenvironment. Methods GSE38206, GSE76826, GSE9653 databases were used to analyze cuproptosis regulators and immune characteristics. To find the genes that were differently expressed, weighted gene co-expression network analysis was employed. We calculated the effectiveness of the random forest model, generalized linear model, and limit gradient lifting to arrive at the best machine prediction model. Nomogram, calibration curve, and decision curve analysis were used to show the anticipated MDD's accuracy. Results This study found that there were activated immune responses and cuproptosis-related genes that were dysregulated in people with MDD compared to healthy controls. Considering the test performance of the learned model and validation on subsequent datasets, the RF model (including OSBPL8, VBP1, MTM1, ELK3, and SLC39A6) was considered to have the best discriminative performance. (AUC = 0.875). Conclusion Our study constructed a prediction model to predict MDD risk and clarified the potential connection between cuproptosis and MDD.
Collapse
Affiliation(s)
- Daoyun Lei
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
| | - Jie Sun
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
| | - Jiangyan Xia
- Department of Anesthesiology, Zhongda Hospital Southeast University (Jiangbei), Nanjing, 210048 Jiangsu, China
- Department of Anesthesiology, Zhongda Hospital Southeast University, Nanjing, 210009 Jiangsu, China
| |
Collapse
|
6
|
Xu Z, Vekaria V, Wang F, Cukor J, Su C, Adekkanattu P, Brandt P, Jiang G, Kiefer RC, Luo Y, Rasmussen LV, Xu J, Xiao Y, Alexopoulos G, Pathak J. Using Machine Learning to Predict Antidepressant Treatment Outcome From Electronic Health Records. PSYCHIATRIC RESEARCH AND CLINICAL PRACTICE 2023; 5:118-125. [PMID: 38077277 PMCID: PMC10698704 DOI: 10.1176/appi.prcp.20220015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/28/2023] Open
Abstract
Objective To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression. Method This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors. Results The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors. Conclusions The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses.
Collapse
Affiliation(s)
| | | | - Fei Wang
- Weill Cornell MedicineNew YorkNew YorkUSA
| | | | - Chang Su
- Temple UniversityPhiladelphiaPennsylvaniaUSA
| | | | | | | | | | - Yuan Luo
- Northwestern UniversityChicagoIllinoisUSA
| | | | - Jie Xu
- University of FloridaGainesvilleFloridaUSA
| | - Yunyu Xiao
- Weill Cornell MedicineNew YorkNew YorkUSA
| | | | | |
Collapse
|
7
|
Galioulline H, Frässle S, Harrison S, Pereira I, Heinzle J, Stephan KE. Predicting Future Depressive Episodes from Resting-State fMRI with Generative Embedding. Neuroimage 2023; 273:119986. [PMID: 36958617 DOI: 10.1016/j.neuroimage.2023.119986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 02/15/2023] [Accepted: 02/25/2023] [Indexed: 03/25/2023] Open
Abstract
After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (MRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N=906) of task-free ("resting state") fMRI data from the UK Biobank. Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three year period, 50% of participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p<0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.
Collapse
Affiliation(s)
- Herman Galioulline
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland.
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Sam Harrison
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Jakob Heinzle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| |
Collapse
|
8
|
Tan J, Ma C, Zhu C, Wang Y, Zou X, Li H, Li J, He Y, Wu C. Prediction models for depression risk among older adults: systematic review and critical appraisal. Ageing Res Rev 2023; 83:101803. [PMID: 36410622 DOI: 10.1016/j.arr.2022.101803] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To provide an overview of prediction models for the risk of major depressive disorder (MDD) among older adults. METHODS We conducted a systematic review combined with a meta-analysis and critical appraisal of published studies on existing geriatric depression risk models. RESULTS The systematic search screened 23,378 titles and abstracts; 14 studies including 20 prediction models were included. A total of 16 predictors were selected in the final model at least twice. Age, physical health, and cognitive function were the most common predictors. Only one model was externally validated, two models were presented with a complete equation, and five models examined the calibration. We found substantial heterogeneity in predictor and outcome definitions across models; important methodological information was often missing. All models were rated at high or unclear risk of bias, primarily due to methodological limitations. The pooled C-statistics of 12 prediction models was 0.83 (95%CI=0.77-0.89). CONCLUSION The usefulness of all models remains unclear due to several methodological limitations. Future studies should focus on methodological quality and external validation of depression risk prediction models.
Collapse
Affiliation(s)
- Jie Tan
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China; School of Public Health, Wuhan University, Wuhan, Hubei, China
| | - Chenxinan Ma
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Chonglin Zhu
- College of Pharmacy, Southwest Medical University, Luzhou, Sichuang, China
| | - Yin Wang
- College of Management Science, Chengdu University of Technology, Chengdu, Sichuan, China
| | - Xiaoshuang Zou
- College of Basic Medicine Science, Shenyang Medical College, Shenyang, Liaoning, China
| | - Han Li
- School of Public Health, Zunyi Medical University, Zunyi, Guizhou, China
| | - Jiarun Li
- School of Basic Medicine, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yanxuan He
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Chenkai Wu
- Global Health Research Center, Duke Kunshan University, Kunshan, Jiangsu, China.
| |
Collapse
|
9
|
Lin S, Wu Y, Fang Y. A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study. BMC Psychiatry 2022; 22:816. [PMID: 36544119 PMCID: PMC9768728 DOI: 10.1186/s12888-022-04439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Our aim was to explore whether a two-step hybrid machine learning model has the potential to discover the onset of depression in home-based older adults. METHODS Depression data (collected in the year 2011, 2013, 2015 and 2018) of home-based older Chinese (n = 2,548) recruited in the China Health and Retirement Longitudinal Study were included in the current analysis. The long short-term memory network (LSTM) was applied to identify the risk factors of participants in 2015 utilizing the first 2 waves of data. Based on the identified predictors, three ML classification algorithms (i.e., gradient boosting decision tree, support vector machine and random forest) were evaluated with a 10-fold cross-validation procedure and a metric of the area under the receiver operating characteristic curve (AUROC) to estimate the depressive outcome. RESULTS Time-varying predictors of the depression were successfully identified by LSTM (mean squared error =0.8). The mean AUCs of the three predictive models had a range from 0.703 to 0.749. Among the prediction variables, self-reported health status, cognition, sleep time, self-reported memory and ADL (activities of daily living) disorder were the top five important variables. CONCLUSIONS A two-step hybrid model based on "LSTM+ML" framework can be robust in predicting depression over a 5-year period with easily accessible sociodemographic and health information.
Collapse
Affiliation(s)
- Shaowu Lin
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Yafei Wu
- grid.12955.3a0000 0001 2264 7233The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102 China ,grid.12955.3a0000 0001 2264 7233Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102 China
| | - Ya Fang
- The State Key Laboratory of Molecular Vaccine and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, China. .,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China. .,Key Laboratory of Health Technology Assessment of Fujian Province, School of Public Health, Xiamen University, Xiamen, 361102, China.
| |
Collapse
|
10
|
Liu D, Liu B, Lin T, Liu G, Yang G, Qi D, Qiu Y, Lu Y, Yuan Q, Shuai SC, Li X, Liu O, Tang X, Shuai J, Cao Y, Lin H. Measuring depression severity based on facial expression and body movement using deep convolutional neural network. Front Psychiatry 2022; 13:1017064. [PMID: 36620657 PMCID: PMC9810804 DOI: 10.3389/fpsyt.2022.1017064] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD. Methods We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients' facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients. Results We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales. Discussion The BDD can effectively measure the current state of patients' depression and its changing trend according to the patient's expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.
Collapse
Affiliation(s)
- Dongdong Liu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Bowen Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Psychiatry, Baoan Mental Health Center, Shenzhen Baoan Center for Chronic Disease Control, Shenzhen, China
| | - Tao Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Guangya Liu
- Integrated Chinese and Western Therapy of Depression Ward, Hunan Brain Hospital, Changsha, China
| | - Guoyu Yang
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Dezhen Qi
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ye Qiu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Yuer Lu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Qinmei Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Stella C. Shuai
- Department of Biological Sciences, Northwestern University, Evanston, IL, United States
| | - Xiang Li
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ou Liu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Xiangdong Tang
- Sleep Medicine Center, Mental Health Center, Department of Respiratory and Critical Care Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Jianwei Shuai
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yuping Cao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hai Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| |
Collapse
|
11
|
Sakal C, Li J, Xiang YT, Li X. Development and validation of the Chinese Geriatric Depression Risk calculator (CGD-risk): A screening tool to identify elderly Chinese with depression. J Affect Disord 2022; 319:428-436. [PMID: 36184985 DOI: 10.1016/j.jad.2022.09.034] [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: 08/15/2022] [Revised: 09/11/2022] [Accepted: 09/15/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND The prevalence of depression among China's elderly is high, but stigma surrounding mental illness and a shortage of psychiatrists limit widespread screening and diagnosis of geriatric depression. We sought to develop a screening tool using easy-to-obtain and minimally sensitive predictors to identify elderly Chinese with depressive symptoms (depression hereafter) for referral to mental health services and determine the most important factors for effective screening. METHODS Using nationally representative survey data, we developed and externally validated the Chinese Geriatric Depression Risk calculator (CGD-Risk). CGD-Risk, a gradient boosting machine learning model, was evaluated based on discrimination (Concordance (C) statistic), calibration, and through a decision curve analysis. We conducted a sensitivity analysis on a cohort of middle-aged Chinese, a sub-group analysis using three data sets, and created predictor importance and partial dependence plots to enhance interpretability. RESULTS A total of 5681 elderly Chinese were included in the development data and 12,373 in the external validation data. CGD-Risk showed good discrimination during internal validation (C: 0.81, 95 % CI 0.79 to 0.84) and external validation (C: 0.77, 95 % CI: 0.76, 0.78). Compared to an alternative screening strategy CGD-Risk would correctly identify 17.8 more elderly with depression per 100 people screened. LIMITATIONS We were only able to externally validate a partial version of CGD-Risk due to differences between the internal and external validation data. CONCLUSIONS CGD-Risk is a clinically viable, minimally sensitive screening tool that could identify elderly Chinese at high risk of depression while circumventing issues of response bias from stigma surrounding emotional openness.
Collapse
Affiliation(s)
- Collin Sakal
- School of Data Science, City University of Hong Kong, Hong Kong, SAR, China
| | - Juan Li
- Center on Aging Psychology, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China
| | - Xinyue Li
- School of Data Science, City University of Hong Kong, Hong Kong, SAR, China.
| |
Collapse
|
12
|
Belvederi Murri M, Cattelani L, Chesani F, Palumbo P, Triolo F, Alexopoulos GS. Risk Prediction Models for Depression in Community-Dwelling Older Adults. Am J Geriatr Psychiatry 2022; 30:949-960. [PMID: 35821215 DOI: 10.1016/j.jagp.2022.05.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 05/26/2022] [Accepted: 05/30/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression. DESIGN Prospective study. SETTING The Survey of Health, Ageing and Retirement in Europe (SHARE) study. PARTICIPANTS Participants were community residing adults aged 55 years or older. MEASUREMENTS The outcome was presence of depression at a 2-year follow up evaluation. Risk factors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator. RESULTS The authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar performance (AUC: 0.730-0.743). In the combined depressed and non-depressed participants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80-0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79-0.82) had satisfactory accuracy. CONCLUSIONS The Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calculator based on the streamlined Manto model is freely available at https://manto.unife.it/ for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels.
Collapse
Affiliation(s)
- Martino Belvederi Murri
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara (MBM), Ferrara, Italy
| | - Luca Cattelani
- Department of Computer Science and Engineering, University of Bologna (LC, FC), Bologna, Italy; Faculty of Medicine and Health Technologies, Tampere University (LC), Tampere, Finland; Institute of Biomedicine, University of Eastern Finland (LC), Kuopio, Finland
| | - Federico Chesani
- Department of Computer Science and Engineering, University of Bologna (LC, FC), Bologna, Italy
| | - Pierpaolo Palumbo
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna (PP), Bologna, Italy
| | - Federico Triolo
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet (FT), Stockholm, Sweden
| | - George S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine (GA), White Plains, NY.
| |
Collapse
|
13
|
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.
Collapse
Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
| | | |
Collapse
|
14
|
Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia. SENSORS 2022; 22:s22072517. [PMID: 35408133 PMCID: PMC9003328 DOI: 10.3390/s22072517] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/26/2022]
Abstract
New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.
Collapse
|
15
|
D'Hotman D, Loh E. AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health Care Inform 2021; 27:bmjhci-2020-100175. [PMID: 33037037 PMCID: PMC7549453 DOI: 10.1136/bmjhci-2020-100175] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/19/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022] Open
Abstract
Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide. Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards. Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.
Collapse
Affiliation(s)
- Daniel D'Hotman
- Oxford Uehiro Centre for Practical Ethics, University of Oxford, Oxford, United Kingdom
| | - Erwin Loh
- Monash Centre for Health Research and Implementation, Monash University, Clayton, Victoria, Australia.,Group Chief Medical Officer, St Vincent's Health Australia Ltd, East Melbourne, Victoria, Australia
| |
Collapse
|
16
|
Detecting suicide risk using knowledge-aware natural language processing and counseling service data. Soc Sci Med 2021; 283:114176. [PMID: 34214846 DOI: 10.1016/j.socscimed.2021.114176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 06/04/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022]
Abstract
RATIONALE Detecting users at risk of suicide in text-based counseling services is essential to ensure that at-risk individuals are flagged and prioritized. OBJECTIVE The objective of this study is to develop a domain knowledge-aware risk assessment (KARA) model to improve our ability of suicide detection in online counseling systems. METHODS We obtained the largest known de-identified dataset from an emotional support system established in Hong Kong, comprising 5682 Cantonese conversations between help-seekers and counselors. Of those, 682 conversations disclosed crisis intentions of suicide. We constructed a suicide-knowledge graph, representing suicide-related domain knowledge as a computer-processible graph. Such knowledge graph was embedded into a deep learning model to improve its ability to identify help-seekers in crisis. As the baseline, a standard NLP model was applied to the same task. 80% of the study samples were randomly sampled to train model parameters. The remaining 20% were used for model validation. Evaluation metrics including precision, recall, and c-statistic were reported. RESULTS Both KARA and the baseline achieved high precision (0.984 and 0.951, shown in Table 2) and high recall (0.942 and 0.947) towards non-crisis cases. For crisis cases, however, KARA model achieved a much higher recall than the baseline (0.870 vs 0.791). The c-statistics of KARA and the baseline were 0.815 and 0.760, respectively. CONCLUSION KARA significantly outperformed standard NLP models, demonstrating good translational value and clinical relevance.
Collapse
|
17
|
Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. J Affect Disord 2021; 279:256-265. [PMID: 33074145 DOI: 10.1016/j.jad.2020.10.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/29/2020] [Accepted: 10/04/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Depression and post-traumatic stress disorder (PTSD) are among the most common psychiatric disorders observed in children and adolescents exposed to sexual abuse. OBJECTIVE The present study aimed to investigate the effects of many factors such as the characteristics of a child, abuse, and the abuser, family type of the child, and the role of social support in the development of psychiatric disorders using machine learning techniques. PARTICIPANTS AND SETTINGS The records of 482 children and adolescents who were determined to have been sexually abused were examined to predict the development of depression and PTSD. METHODS Each child was evaluated by a child and adolescent psychiatrist in the psychiatric aspect according to the DSM-V. Through the data of both groups, a predictive model was established based on a random forest classifier. RESULTS The mean values and standard deviation of the 10-k cross-validated results were obtained as accuracy: 0.82% (+/- 0.19%), F1: 0.81% (+/- 0.19%), precision: 0.81% (+/- 0.19%), recall: 0.80% (+/- 0.19%) for children with depression; and accuracy: 0.72% (+/- 0.12%), F1: 0.71% (+/- 0.12%), precision: 0.72% (+/- 0.12%), recall: 0.71% (+/- 0.12%) for children with PTSD, respectively. ROC curves were drawn for both, and the AUC results were obtained as 0.88 for major depressive disorder and 0.76 for PTSD. CONCLUSIONS Machine learning techniques are powerful methods that can be used to predict disorders that may develop after sexual abuse. The results should be supported by studies with larger samples, which are repeated and applied to other risk groups.
Collapse
Affiliation(s)
- Emel Sari Gokten
- Assoc Prof of Child and Adolescent Psychiatry, Uskudar University Medical Faculty, Istanbul, Turkey.
| | - Caglar Uyulan
- Assist Prof of Mechatronics Engineering Department, Zonguldak Bulent Ecevit University Faculty of Engineering, Zonguldak, Turkey.
| |
Collapse
|
18
|
Xu Z, Wang F, Adekkanattu P, Bose B, Vekaria V, Brandt P, Jiang G, Kiefer RC, Luo Y, Pacheco JA, Rasmussen LV, Xu J, Alexopoulos G, Pathak J. Subphenotyping depression using machine learning and electronic health records. Learn Health Syst 2020; 4:e10241. [PMID: 33083540 PMCID: PMC7556423 DOI: 10.1002/lrh2.10241] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 06/08/2020] [Accepted: 07/15/2020] [Indexed: 12/19/2022] Open
Abstract
Objective To identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications. Materials and Methods Using EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics. Results Using the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype. Conclusion Computationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.
Collapse
Affiliation(s)
- Zhenxing Xu
- Weill Cornell Medicine New York New York USA
| | - Fei Wang
- Weill Cornell Medicine New York New York USA
| | | | | | | | | | | | | | - Yuan Luo
- Northwestern University Chicago Illinois USA
| | | | | | - Jie Xu
- Weill Cornell Medicine New York New York USA
| | | | | |
Collapse
|