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Li Y, Song Y, Sui J, Greiner R, Li XM, Greenshaw AJ, Liu YS, Cao B. Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study. J Affect Disord 2024; 357:148-155. [PMID: 38670463 DOI: 10.1016/j.jad.2024.04.098] [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: 07/21/2023] [Revised: 03/20/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders. METHODS We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone. RESULTS Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors. LIMITATION While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy. CONCLUSION Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.
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Affiliation(s)
- Yutong Li
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Yipeng Song
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Jie Sui
- School of Psychology, University of Aberdeen, Aberdeen, UK
| | - Russell Greiner
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Xin-Min Li
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Andrew J Greenshaw
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Yang S Liu
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.
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Al-Omran K, Khan E. Predicting medical waste generation and associated factors using machine learning in the Kingdom of Bahrain. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:38343-38357. [PMID: 38801607 DOI: 10.1007/s11356-024-33773-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/19/2024] [Indexed: 05/29/2024]
Abstract
Effective planning and managing medical waste necessitate a crucial focus on both the public and private healthcare sectors. This study uses machine learning techniques to estimate medical waste generation and identify associated factors in a representative private and a governmental hospital in Bahrain. Monthly data spanning from 2018 to 2022 for the private hospital and from 2019 to February 2023 for the governmental hospital was utilized. The ensemble voting regressor was determined as the best model for both datasets. The model of the governmental hospital is robust and successful in explaining 90.4% of the total variance.Similarly, for the private hospital, the model variables are able to explain 91.7% of the total variance. For the governmental hospital, the significant features in predicting medical waste generation were found to be the number of inpatients, population, surgeries, and outpatients, in descending order of importance. In the case of the private hospital, the order of feature importance was the number of inpatients, deliveries, personal income, surgeries, and outpatients. These findings provide insights into the factors influencing medical waste generation in the studied hospitals and highlight the effectiveness of the ensemble voting regressor model in predicting medical waste quantities.
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Affiliation(s)
- Khadija Al-Omran
- Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain.
- School of Logistics and Maritime Studies, Faculty of Business and Logistics, Bahrain Polytechnic, Isa Town, 33349, Kingdom of Bahrain.
| | - Ezzat Khan
- Environment and Sustainable Development, College of Science, University of Bahrain, Sakhir, 32038, Kingdom of Bahrain
- Department of Chemistry, University of Malakand, Lower Dir, Chakdara, 18800, Khyber Pakhtunkhwa, Pakistan
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Xu Y, Zhong H, Ying S, Liu W, Chen G, Luo X, Li G. Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:8639. [PMID: 37896732 PMCID: PMC10611358 DOI: 10.3390/s23208639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/10/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.
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Affiliation(s)
- Yanting Xu
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Hongyang Zhong
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Shangyan Ying
- College of Engineering, Zhejiang Normal University, Jinhua 321004, China; (Y.X.); (S.Y.)
| | - Wei Liu
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Guibin Chen
- College of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China; (H.Z.); (W.L.); (G.C.)
| | - Xiaodong Luo
- The Second Hospital of Jinhua, Jinhua 321016, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321004, China
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Han S, Sohn TJ, Ng BP, Park C. Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach. Sci Rep 2023; 13:13491. [PMID: 37596346 PMCID: PMC10439193 DOI: 10.1038/s41598-023-40552-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017-2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model's performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.
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Affiliation(s)
- Sola Han
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Ted J Sohn
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA
| | - Boon Peng Ng
- College of Nursing, University of Central Florida, Orlando, FL, USA
- Disability, Aging, and Technology Cluster, University of Central Florida, Orlando, FL, USA
| | - Chanhyun Park
- Health Outcomes Division, College of Pharmacy, The University of Texas at Austin, Austin, TX, 78712, USA.
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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Joyce DW, Kormilitzin A, Smith KA, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. NPJ Digit Med 2023; 6:6. [PMID: 36653524 PMCID: PMC9849399 DOI: 10.1038/s41746-023-00751-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
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Affiliation(s)
- Dan W. Joyce
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.10025.360000 0004 1936 8470Institute of Population Health, Department of Primary Care and Mental Health, University of Liverpool, Liverpool, L69 3GF UK
| | - Andrey Kormilitzin
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK
| | - Katharine A. Smith
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
| | - Andrea Cipriani
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
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Nguyen HV, Byeon H. LIME-based ensemble machine for predicting performance status of patients with liver cancer. Digit Health 2023; 9:20552076231211636. [PMID: 38025102 PMCID: PMC10631338 DOI: 10.1177/20552076231211636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Objective The Eastern Cooperative Oncology Group performance status (ECOG PS) is a widely recognized measure used to assess the functional abilities of cancer patients and predict their prognosis. It plays a crucial role in guiding treatment decisions made by physicians. This study aimed to build a stacking ensemble-based prognosis predictor model for predicting the ECOG PS of a liver cancer patient undergoing treatment. Methods We used Light Gradient Boosting Machine (LightGBM) as the meta-model, and five base models, including Random Forest (RF), Extra Trees (ET), AdaBoost (Ada), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). After preprocessing the data and applying feature selection method, the stacking ensemble model was trained using 1622 liver cancer patients' data and 46 variables. We also integrated the stacking ensemble model with a LIME-based explainable model to obtain model prediction explainability. Results According to the research, the best combination of the stacking ensemble model is ET + XGBoost + RF + GBM + Ada + LightGBM and achieved a ROC AUC of 0.9826 on the training set and 0.9675 on the test set. Conclusions This explainable stacking ensemble model can become a helpful tool for objectively predicting ECOG PS in liver cancer patients and aiding healthcare practitioners to adapt their treatment approach more effectively.
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Affiliation(s)
- Hung Viet Nguyen
- Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea
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Ahmadi M, Nopour R. Clinical decision support system for quality of life among the elderly: an approach using artificial neural network. BMC Med Inform Decis Mak 2022; 22:293. [DOI: 10.1186/s12911-022-02044-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 11/09/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Background
Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors.
Methods
In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL.
Results
Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity = 0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly.
Conclusion
The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.
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Asghari Varzaneh Z, Shanbehzadeh M, Kazemi-Arpanahi H. Prediction of successful aging using ensemble machine learning algorithms. BMC Med Inform Decis Mak 2022; 22:258. [PMID: 36192713 PMCID: PMC9527392 DOI: 10.1186/s12911-022-02001-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 09/20/2022] [Indexed: 11/21/2022] Open
Abstract
Background Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA.
Methods In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models. Results The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure. Conclusions Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.
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Affiliation(s)
- Zahra Asghari Varzaneh
- Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran. .,Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran.
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Shen Z, Li G, Fang J, Zhong H, Wang J, Sun Y, Shen X. Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework. SENSORS (BASEL, SWITZERLAND) 2022; 22:5420. [PMID: 35891100 PMCID: PMC9320264 DOI: 10.3390/s22145420] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/12/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized anxiety disorder (GAD). To this end, this study intends to develop an analysis framework for automatic GAD detection through incorporating multidimensional EEG feature extraction and machine learning techniques. Specifically, resting-state EEG signals with a duration of 10 min were obtained from 45 patients with GAD and 36 healthy controls (HC). Then, an analysis framework of multidimensional EEG characteristics (including univariate power spectral density (PSD) and fuzzy entropy (FE), and multivariate functional connectivity (FC), which can decode the EEG information from three different dimensions) were introduced for extracting aberrated multidimensional EEG features via statistical inter-group comparisons. These aberrated features were subsequently fused and fed into three previously validated machine learning methods to evaluate classification performance for automatic patient detection. We showed that patients exhibited a significant increase in beta rhythm and decrease in alpha1 rhythm of PSD, together with the reduced long-range FC between frontal and other brain areas in all frequency bands. Moreover, these aberrated features contributed to a very good classification performance with 97.83 ± 0.40% of accuracy, 97.55 ± 0.31% of sensitivity, 97.78 ± 0.36% of specificity, and 97.95 ± 0.17% of F1. These findings corroborate previous hypothesis of disconnectivity in psychiatric disorders and further shed light on distribution patterns of aberrant spatio-spectral EEG characteristics, which may lead to potential application of automatic diagnosis of GAD.
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Affiliation(s)
- Zhongxia Shen
- School of Medicine, Southeast University, Nanjing 210096, China;
- Sleep Medical Center, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou 313000, China
| | - Gang Li
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
| | - Jiaqi Fang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Hongyang Zhong
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Jie Wang
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua 321017, China; (J.F.); (H.Z.); (J.W.)
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education of China, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China;
| | - Xinhua Shen
- Sleep Medical Center, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou 313000, China
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Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey. MATHEMATICS 2022. [DOI: 10.3390/math10142466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.
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Predicting High-Risk Groups for COVID-19 Anxiety Using AdaBoost and Nomogram: Findings from Nationwide Survey in South Korea. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11219865] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
People living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage in increasing the classification accuracy of single learners and (2) developed a nomogram for predicting high-risk groups of coronavirus anxiety while considering both prediction performance and interpretability based on this. Among 210,606 adults (95,287 males and 115,319 females) in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19. The AdaBoost model confirmed that education level, awareness of neighbors/colleagues’ COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (classification accuracy = 0.812, precision = 0.761, recall = 0.812, AUC = 0.688, and F-1 score = 0.740). Prospective or retrospective cohort studies are required to causally identify the characteristics of anxiety disorders targeting high-risk COVID-19 anxiety groups identified in this study.
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Byeon H. Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea. Front Psychiatry 2021; 12:773290. [PMID: 35069283 PMCID: PMC8777037 DOI: 10.3389/fpsyt.2021.773290] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open
Abstract
This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.
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Affiliation(s)
- Haewon Byeon
- Department of Medical Big Data, College of Artificial Intelligence Convergence, Inje University, Gimhae, South Korea
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