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Xu D, Xu Z. Machine learning applications in preventive healthcare: A systematic literature review on predictive analytics of disease comorbidity from multiple perspectives. Artif Intell Med 2024; 156:102950. [PMID: 39163727 DOI: 10.1016/j.artmed.2024.102950] [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: 10/25/2023] [Revised: 06/17/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024]
Abstract
Artificial intelligence is constantly revolutionizing biomedical research and healthcare management. Disease comorbidity is a major threat to the quality of life for susceptible groups, especially middle-aged and elderly patients. The presence of multiple chronic diseases makes precision diagnosis challenging to realize and imposes a heavy burden on the healthcare system and economy. Given an enormous amount of accumulated health data, machine learning techniques show their capability in handling this puzzle. The present study conducts a review to uncover current research efforts in applying these methods to understanding comorbidity mechanisms and making clinical predictions considering these complex patterns. A descriptive metadata analysis of 791 unique publications aims to capture the overall research progression between January 2012 and June 2023. To delve into comorbidity-focused research, 61 of these scientific papers are systematically assessed. Four predictive analytics of tasks are detected: disease comorbidity data extraction, clustering, network, and risk prediction. It is observed that some machine learning-driven applications address inherent data deficiencies in healthcare datasets and provide a model interpretation that identifies significant risk factors of comorbidity development. Based on insights, both technical and practical, gained from relevant literature, this study intends to guide future interests in comorbidity research and draw conclusions about chronic disease prevention and diagnosis with managerial implications.
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Affiliation(s)
- Duo Xu
- School of Economics and Management, Southeast University, Nanjing 211189, China.
| | - Zeshui Xu
- School of Economics and Management, Southeast University, Nanjing 211189, China; Business School, Sichuan University, Chengdu 610064, China.
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Pan Y, Zhang X, Wen X, Yuan N, Guo L, Shi Y, Jia Y, Guo Y, Hao F, Qu S, Chen Z, Yang L, Wang X, Liu Y. Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1. Sleep Med 2024; 119:556-564. [PMID: 38810481 DOI: 10.1016/j.sleep.2024.05.045] [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: 12/21/2023] [Revised: 05/04/2024] [Accepted: 05/22/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.
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Affiliation(s)
- Yuanhang Pan
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xinbo Zhang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xinyu Wen
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Na Yuan
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Li Guo
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yifan Shi
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yuanyuan Jia
- Encerebropathy Department, No.2, Baoji Hospital of Traditional Chinese Medicine, Baoji, PR China.
| | - Yanzhao Guo
- Encerebropathy Department, No.10, Xi'an Hospital of Traditional Chinese Medicine, Xi'an, PR China.
| | - Fengli Hao
- Department of Neurology, Xi'an Daxing Hospital, Xi'an, PR China.
| | - Shuyi Qu
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Ze Chen
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Lei Yang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Xiaoli Wang
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
| | - Yonghong Liu
- Department of Neurology, Xijing Hospital, Air Force Medical University, Xi'an, PR China.
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Marrie RA. The Barancik lecture: Comorbidity in multiple sclerosis-Looking backward, looking forward. Mult Scler 2023; 29:1049-1056. [PMID: 37125430 PMCID: PMC10413784 DOI: 10.1177/13524585231167740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/11/2023] [Accepted: 03/19/2023] [Indexed: 05/02/2023]
Abstract
Comorbidity is highly prevalent in people with multiple sclerosis (MS) throughout their disease course. In the last 15 years, our understanding of the association between comorbidity and outcomes such as relapses, disability progressive, health-related quality of life, health care use, and mortality has grown substantially. The broad adverse impacts of comorbidity on these outcomes point to the need to prevent and treat comorbidity effectively in people with MS. This requires having the necessary tools to evaluate comorbidity, an understanding of how MS affects management of comorbidity now, testing of interventions tailored to people with MS, and determining the best models of care to optimize comorbidity management.
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Affiliation(s)
- Ruth Ann Marrie
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada/Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
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Abdulla H, Maalouf M, Jelinek HF. Machine Learning for the Prediction of Depression Progression from Inflammation Markers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082683 DOI: 10.1109/embc40787.2023.10340436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Major depressive disorder is one of the major contributors to disability worldwide with an estimated prevalence of 4%. Depression is a heterogeneous disease often characterized by an undefined pathogenesis and multifactorial phenotype that complicate diagnosis and follow-up. Translational research and identification of objective biomarkers including inflammation can assist clinicians in diagnosing depression and disease progression. Investigating inflammation markers using machine learning methods combines recent understanding of the pathogenesis of depression associated with inflammatory changes as part of chronic disease progression that aims to highlight complex interactions. In this paper, 721 patients attending a diabetes health screening clinic (DiabHealth) were classified into no depression (none) to minimal depression (none-minimal), mild depression, and moderate to severe depression (moderate-severe) based on the Patient Health Questionnaire (PHQ-9). Logistic Regression, K-nearest Neighbors, Support Vector Machine, Random Forest, Multi-layer Perceptron, and Extreme Gradient Boosting were applied and compared to predict depression level from inflammatory marker data that included C-reactive protein (CRP), Interleukin (IL)-6, IL-1β, IL-10, Complement Component 5a (C5a), D-Dimer, Monocyte Chemoattractant Protein (MCP)-1, and Insulin-like Growth Factor (IGF)-1. MCP-1 and IL-1β were the most significant inflammatory markers for the classification performance of depression level. Extreme Gradient Boosting outperformed the models achieving the highest accuracy and Area Under the Receiver Operator Curve (AUC) of 0.89 and 0.95, respectively.Clinical Relevance- The findings of this study show the potential of machine learning models to aid in clinical practice, leading to a more objective assessment of depression level based on the involvement of MCP-1 and IL-1β inflammatory markers with disease progression.
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Marrie RA, Fisk JD, Fitzgerald K, Kowalec K, Maxwell C, Rotstein D, Salter A, Tremlett H. Etiology, effects and management of comorbidities in multiple sclerosis: recent advances. Front Immunol 2023; 14:1197195. [PMID: 37325663 PMCID: PMC10266935 DOI: 10.3389/fimmu.2023.1197195] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/19/2023] [Indexed: 06/17/2023] Open
Abstract
Comorbid conditions commonly affect people with multiple sclerosis (MS). Population-based studies indicate that people with MS have an increased incidence of ischemic heart disease, cerebrovascular disease, peripheral vascular disease, and psychiatric disorders as compared to people without MS. People with MS from underrepresented minority and immigrant groups have higher comorbidity burdens. Comorbidities exert effects throughout the disease course, from symptom onset through diagnosis to the end of life. At the individual level, comorbidity is associated with higher relapse rates, greater physical and cognitive impairments, lower health-related quality of life, and increased mortality. At the level of the health system and society, comorbidity is associated with increased health care utilization, costs and work impairment. A nascent literature suggests that MS affects outcomes from comorbidities. Comorbidity management needs to be integrated into MS care, and this would be facilitated by determining optimal models of care.
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Affiliation(s)
- Ruth Ann Marrie
- Department of Internal Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - John D. Fisk
- Nova Scotia Health and the Departments of Psychiatry, Psychology & Neuroscience, and Medicine, Dalhousie University, Halifax, NS, Canada
| | - Kathryn Fitzgerald
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Kaarina Kowalec
- College of Pharmacy, University of Manitoba, Winnipeg, MB, Canada
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Colleen Maxwell
- Schools of Pharmacy and Public Health & Health Systems, University of Waterloo, Waterloo, ON, Canada
| | - Dalia Rotstein
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- St. Michael’s Hospital, Toronto, ON, Canada
| | - Amber Salter
- Department of Neurology, UT Southwestern, Dallas, TX, United States
| | - Helen Tremlett
- Department of Medicine (Neurology) and the Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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Alsaleh MM, Allery F, Choi JW, Hama T, McQuillin A, Wu H, Thygesen JH. Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. Int J Med Inform 2023; 175:105088. [PMID: 37156169 DOI: 10.1016/j.ijmedinf.2023.105088] [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: 02/06/2023] [Revised: 03/23/2023] [Accepted: 05/01/2023] [Indexed: 05/10/2023]
Abstract
OBJECTIVE Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities.
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Affiliation(s)
- Mohanad M Alsaleh
- Institute of Health Informatics, University College London, London, UK; Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah, Saudi Arabia.
| | - Freya Allery
- Institute of Health Informatics, University College London, London, UK
| | - Jung Won Choi
- Institute of Health Informatics, University College London, London, UK
| | - Tuankasfee Hama
- Institute of Health Informatics, University College London, London, UK
| | | | - Honghan Wu
- Institute of Health Informatics, University College London, London, UK
| | - Johan H Thygesen
- Institute of Health Informatics, University College London, London, UK
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Lee KS, Ham BJ. Machine Learning on Early Diagnosis of Depression. Psychiatry Investig 2022; 19:597-605. [PMID: 36059048 PMCID: PMC9441463 DOI: 10.30773/pi.2022.0075] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University Anam Hospital, Seoul, Republic of Korea
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Predictive utility of symptom measures in classifying anxiety and depression: A machine-learning approach. Psychiatry Res 2022; 312:114534. [PMID: 35381506 PMCID: PMC9117511 DOI: 10.1016/j.psychres.2022.114534] [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: 10/30/2021] [Revised: 03/12/2022] [Accepted: 03/26/2022] [Indexed: 11/24/2022]
Abstract
Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent, co-occurring disorders with significant symptom overlap, posing challenges in accurately distinguishing and diagnosing these disorders. The tripartite model proposes that anxious arousal is specific to anxiety and anhedonia is specific to depression, though anxious apprehension may play a greater role in GAD than anxious arousal. The present study tested the efficacy of the Mood and Anxiety Symptom Questionnaire anhedonic depression (MASQ-AD) and anxious arousal (MASQ-AA) scales and the Penn State Worry Questionnaire (PSWQ) in identifying lifetime or current MDD, current major depressive episode (MDE), and GAD using binary support vector machine learning algorithms in an adult sample (n = 150). The PSWQ and MASQ-AD demonstrated predictive utility in screening for and identification of GAD and current MDE respectively, with the MASQ-AD eight-item subscale outperforming the MASQ-AD 14-item subscale. The MASQ-AA did not predict MDD, current MDE, or GAD, and the MASQ-AD did not predict current or lifetime MDD. The PSWQ and MASQ-AD are efficient and accurate screening tools for GAD and current MDE. Results support the tripartite model in that anhedonia is unique to depression, but inclusion of anxious apprehension as a separate dimension of anxiety is warranted.
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Delpino F, Costa Â, Farias S, Chiavegatto Filho A, Arcêncio R, Nunes B. Machine learning for predicting chronic diseases: a systematic review. Public Health 2022; 205:14-25. [DOI: 10.1016/j.puhe.2022.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 10/26/2021] [Accepted: 01/11/2022] [Indexed: 12/12/2022]
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Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions. ELECTRONICS 2022. [DOI: 10.3390/electronics11071111] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense psychological effects on people’s minds worldwide. The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. Machine learning (ML) has been accredited as an efficient approach for analyzing the massive amount of data in the healthcare domain. ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. This review paper enlists different machine learning algorithms used to detect and diagnose depression. The ML-based depression detection algorithms are categorized into three classes, classification, deep learning, and ensemble. A general model for depression diagnosis involving data extraction, pre-processing, training ML classifier, detection classification, and performance evaluation is presented. Moreover, it presents an overview to identify the objectives and limitations of different research studies presented in the domain of depression detection. Furthermore, it discussed future research possibilities in the field of depression diagnosis.
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Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2021; 21:7786. [PMID: 34883787 PMCID: PMC8659723 DOI: 10.3390/s21237786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022]
Abstract
The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
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Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Aanchal Thakur
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Santosh Saxena
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Nandita Jassal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Chirag Patel
- Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India;
| | - Kirit Modi
- Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India;
| | - Pooja Shah
- Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India;
| | - Rahul Joshi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Sudhanshu Gonge
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Kalyani Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Prachi Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
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Nguyen B, Nigro M, Rueda A, Kolappan S, Bhat V, Krishnan S. Feature Analysis and Hierarchical Classification of Anxiety Severity during early COVID-19. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1678-1681. [PMID: 34891608 DOI: 10.1109/embc46164.2021.9629537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Distress, confusion, and anger are common responses to COVID-19. Statistics Canada created the Canadian Perspectives Survey Series (CPSS) to understand social issues and effects of COVID-19 on the Canadian labour force (LF). The evaluation of the health and health-related behaviours were done through surveys collected between April and July. Features are composed of 4600 participants and 62 questions, which include the General Anxiety Disorder (GAD)-7 questionnaire. This work proposes the use of CPSS2 survey data characteristics to identify the level of anxiety within the Canadian population during early stages of COVID-19 and is validated with the use of GAD-7 questionnaire. Minimum redundancy maximum relevance (mRMR) is applied to select the top 20 features to represent user anxiety. During classification, decision tree (DT) and support vector machine (SVM) are used to test the separation of anxiety severity. Hierarchical classification was used which separated the anxiety severity labels into different test sets and classified accordingly. We employ SVM for binary classification with 10-fold cross validation to separate the labels of Minimal and Severe anxiety to achieve an overall accuracy of 94.77±0.05%. After analysis, a subset of the reduced feature set can be represented as pseudo passive (PP) data, which are passive sensors that can augment qualitative data. The accurate classification provides proxy on what gives rise to anxiety, as well as the ability to provide early interventions. Future works can implement passive sensors to augment PP data and further understand why people cope this way.
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de Jong Y, van der Willik EM, Milders J, Meuleman Y, Morton RL, Dekker FW, van Diepen M. Person centred care provision and care planning in chronic kidney disease: which outcomes matter? A systematic review and thematic synthesis of qualitative studies : Care planning in CKD: which outcomes matter? BMC Nephrol 2021; 22:309. [PMID: 34517825 PMCID: PMC8438879 DOI: 10.1186/s12882-021-02489-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 07/29/2021] [Indexed: 11/23/2022] Open
Abstract
RATIONALE & OBJECTIVE Explore priorities related to outcomes and barriers of adults with chronic kidney disease (CKD) regarding person centred care and care planning. STUDY DESIGN Systematic review of qualitative studies. SEARCH STRATEGY & SOURCES In July 2018 six bibliographic databases, and reference lists of included articles were searched for qualitative studies that included adults with CKD stages 1-5, not on dialysis or conservative management, without a previous kidney transplantation. ANALYTICAL APPROACH Three independent reviewers extracted and inductively coded data using thematic synthesis. Reporting quality was assessed using the COREQ and the review reported according to PRISMA and ENTREQ statements. RESULTS Forty-six studies involving 1493 participants were eligible. The period after diagnosis of CKD is characterized by feelings of uncertainty, social isolation, financial burden, resentment and fear of the unknown. Patients show interest in ways to return to normality and remain in control of their health in order to avoid further deterioration of kidney function. However, necessary information is often unavailable or incomprehensible. Although patients and healthcare professionals share the predominant interest of whether or not dialysis or transplantation is necessary, patients value many more outcomes that are often unrecognized by their healthcare professionals. We identified 4 themes with 6 subthemes that summarize these findings: 'pursuing normality and control' ('pursuing normality'; 'a search for knowledge'); 'prioritizing outcomes' ('reaching kidney failure'; 'experienced health'; 'social life'; 'work and economic productivity'); 'predicting the future'; and 'realising what matters'. Reporting quality was moderate for most included studies. LIMITATIONS Exclusion of non-English articles. CONCLUSIONS The realisation that patients' priorities do not match those of the healthcare professionals, in combination with the prognostic ambiguity, confirms fatalistic perceptions of not being in control when living with CKD. These insights may contribute to greater understanding of patients' perspectives and a more person-centred approach in healthcare prioritization and care planning within CKD care.
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Affiliation(s)
- Ype de Jong
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands.
- Department of Internal Medicine, Leiden University Medical Centre, Leiden, The Netherlands.
| | - Esmee M van der Willik
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Jet Milders
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Yvette Meuleman
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Rachael L Morton
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Friedo W Dekker
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
| | - Merel van Diepen
- Department of Clinical Epidemiology, Leiden University Medical Centre, Albinusdreef 2, 2333, ZA, Leiden, The Netherlands
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14
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Jin H, Chien S, Meijer E, Khobragade P, Lee J. Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study. JMIR Ment Health 2021; 8:e27113. [PMID: 33970122 PMCID: PMC8145077 DOI: 10.2196/27113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/11/2021] [Accepted: 04/17/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. OBJECTIVE This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. METHODS Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. RESULTS Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. CONCLUSIONS The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.
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Affiliation(s)
- Haomiao Jin
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Sandy Chien
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Erik Meijer
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
- RAND Corporation, Santa Monica, CA, United States
| | - Pranali Khobragade
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
- RAND Corporation, Santa Monica, CA, United States
- Department of Economics, University of Southern California, Los Angeles, CA, United States
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15
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Morelli D, Dolezalova N, Ponzo S, Colombo M, Plans D. Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population. Front Psychiatry 2021; 12:689026. [PMID: 34483986 PMCID: PMC8414584 DOI: 10.3389/fpsyt.2021.689026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 07/13/2021] [Indexed: 11/29/2022] Open
Abstract
The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.
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Affiliation(s)
- Davide Morelli
- Huma Therapeutics Ltd., London, United Kingdom.,Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | | | - Sonia Ponzo
- Huma Therapeutics Ltd., London, United Kingdom
| | | | - David Plans
- Huma Therapeutics Ltd., London, United Kingdom.,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom.,Initiative in the Digital Economy at Exeter (INDEX) Group, Department of Science, Innovation, Technology, and Entrepreneurship, University of Exeter, Exeter, United Kingdom
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16
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Wang C, Zhao H, Zhang H. Chinese College Students Have Higher Anxiety in New Semester of Online Learning During COVID-19: A Machine Learning Approach. Front Psychol 2020; 11:587413. [PMID: 33343461 PMCID: PMC7744590 DOI: 10.3389/fpsyg.2020.587413] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 11/16/2020] [Indexed: 12/13/2022] Open
Abstract
The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale (SAS) twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 (15.7%, Mean = 58.45, SD = 7.81) and 221 (18.86%, Mean = 57.68, SD = 7.58) students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test (P < 0.05). Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies (P < 0.05). The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students.
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Affiliation(s)
- Chongying Wang
- Department of Social Psychology, Zhou Enlai School of Government, Nankai University, Tianjin, China
| | - Hong Zhao
- Department of General Computer, College of Computer Science, Nankai University, Tianjin, China
| | - Haoran Zhang
- Department of General Computer, College of Computer Science, Nankai University, Tianjin, China
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