1
|
Shaha TR, Begum M, Uddin J, Torres VY, Iturriaga JA, Ashraf I, Samad MA. Feature group partitioning: an approach for depression severity prediction with class balancing using machine learning algorithms. BMC Med Res Methodol 2024; 24:123. [PMID: 38831346 PMCID: PMC11145774 DOI: 10.1186/s12874-024-02249-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In contemporary society, depression has emerged as a prominent mental disorder that exhibits exponential growth and exerts a substantial influence on premature mortality. Although numerous research applied machine learning methods to forecast signs of depression. Nevertheless, only a limited number of research have taken into account the severity level as a multiclass variable. Besides, maintaining the equality of data distribution among all the classes rarely happens in practical communities. So, the inevitable class imbalance for multiple variables is considered a substantial challenge in this domain. Furthermore, this research emphasizes the significance of addressing class imbalance issues in the context of multiple classes. We introduced a new approach Feature group partitioning (FGP) in the data preprocessing phase which effectively reduces the dimensionality of features to a minimum. This study utilized synthetic oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic (ADASYN), for class balancing. The dataset used in this research was collected from university students by administering the Burn Depression Checklist (BDC). For methodological modifications, we implemented heterogeneous ensemble learning stacking, homogeneous ensemble bagging, and five distinct supervised machine learning algorithms. The issue of overfitting was mitigated by evaluating the accuracy of the training, validation, and testing datasets. To justify the effectiveness of the prediction models, balanced accuracy, sensitivity, specificity, precision, and f1-score indices are used. Overall, comprehensive analysis demonstrates the discrimination between the Conventional Depression Screening (CDS) and FGP approach. In summary, the results show that the stacking classifier for FGP with SMOTE approach yields the highest balanced accuracy, with a rate of 92.81%. The empirical evidence has demonstrated that the FGP approach, when combined with the SMOTE, able to produce better performance in predicting the severity of depression. Most importantly the optimization of the training time of the FGP approach for all of the classifiers is a significant achievement of this research.
Collapse
Affiliation(s)
- Tumpa Rani Shaha
- Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, 1707, Bangladesh
- Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh
| | - Momotaz Begum
- Department of Computer Science and Engineering, Dhaka University of Engineering & Technology, Gazipur, 1707, Bangladesh.
| | - Jia Uddin
- AI and Big Data Department, Woosong University, Daejeon, 34606, South Korea
| | - Vanessa Yélamos Torres
- Universidad Europea del Atlántico, Santander, 39011, Spain
- Universidad Internacional Iberoamericana Campeche, Campeche, 24560, México
- Universidad de La Romana, La Romana, República Dominicana
| | - Josep Alemany Iturriaga
- Universidad Europea del Atlántico, Santander, 39011, Spain
- Universidad Internacional Iberoamericana Arecibo, Puerto Rico, 00613, USA
- Universidade Internacional do Cuanza, Cuito, Bié, Angola
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea.
| | - Md Abdus Samad
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea.
| |
Collapse
|
2
|
Greco C, Raimo G, Amorese T, Cuciniello M, Mcconvey G, Cordasco G, Faundez-Zanuy M, Vinciarelli A, Callejas-Carrion Z, Esposito A. Discriminative Power of Handwriting and Drawing Features in Depression. Int J Neural Syst 2024; 34:2350069. [PMID: 38009869 DOI: 10.1142/s0129065723500697] [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] [Indexed: 11/29/2023]
Abstract
This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders ([Formula: see text]), and patients with a clinical diagnosis of depression ([Formula: see text]). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation.
Collapse
Affiliation(s)
- Claudia Greco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gennaro Raimo
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Terry Amorese
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marialucia Cuciniello
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Gavin Mcconvey
- Action Mental Health, 27 Jubilee Rd, BT23 4YH, Newtownards, UK
| | - Gennaro Cordasco
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| | - Marcos Faundez-Zanuy
- Tecnocampus Universitat Pompeu Fabra, Carrer d'Ernest Lluch 32 Mataro, Barcelona 08302, Spain
| | - Alessandro Vinciarelli
- University of Glasgow, School of Computing Science, 18 Lilybank Gardens Glasgow, G12,8RZ, Scotland
| | - Zoraida Callejas-Carrion
- Department of Languages and Computer Systems, Universidad de Granada, Periodista Daniel Saucedo Aranda Granada, 18071, Spain
| | - Anna Esposito
- Department of Psychology, Università della Campania "Luigi Vanvitelli", Viale Ellittico 31 Caserta, 81000, Italy
| |
Collapse
|
3
|
Nashwan AJ, Gharib S, Alhadidi M, El-Ashry AM, Alamgir A, Al-Hassan M, Khedr MA, Dawood S, Abufarsakh B. Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care. Issues Ment Health Nurs 2023; 44:1020-1034. [PMID: 37850937 DOI: 10.1080/01612840.2023.2263579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.
Collapse
Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Suzan Gharib
- Nursing Department, Al-Khaldi Hospital, Amman, Jordan
| | - Majdi Alhadidi
- Psychiatric & Mental Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | | | | | | | | | - Shaimaa Dawood
- Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | | |
Collapse
|
4
|
Pettorruso M, Guidotti R, d'Andrea G, De Risio L, D'Andrea A, Chiappini S, Carullo R, Barlati S, Zanardi R, Rosso G, De Filippis S, Di Nicola M, Andriola I, Marcatili M, Nicolò G, Martiadis V, Bassetti R, Nucifora D, De Fazio P, Rosenblat JD, Clerici M, Maria Dell'Osso B, Vita A, Marzetti L, Sensi SL, Di Lorenzo G, McIntyre RS, Martinotti G. Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING). Psychiatry Res 2023; 327:115378. [PMID: 37574600 DOI: 10.1016/j.psychres.2023.115378] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/15/2023]
Abstract
Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.
Collapse
Affiliation(s)
- Mauro Pettorruso
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Roberto Guidotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Giacomo d'Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy.
| | - Luisa De Risio
- Department of Mental Health and Addiction, ASL Roma 5, Rome, Italy
| | - Antea D'Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefania Chiappini
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Rosalba Carullo
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefano Barlati
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Raffaella Zanardi
- Mood Disorder Unit, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy; Department of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Gianluca Rosso
- Department of Neurosciences Rita Levi Montalcini, University of Torino, Turin, Italy
| | | | - Marco Di Nicola
- Department of Neurosciences, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome; Department of Psychiatry, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome
| | | | - Matteo Marcatili
- Department of Mental Health and Addiction, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | - Giuseppe Nicolò
- Department of Mental Health and Addiction, ASL Roma 5, Rome, Italy
| | | | - Roberta Bassetti
- Department of Mental Health and Addiction Services, Niguarda Hospital, Milan, Italy
| | | | - Pasquale De Fazio
- Psychiatry Unit, Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Joshua D Rosenblat
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Braxia Health, Canadian Centre for Rapid Treatment Excellence (CRTCE), Mississauga, ON, Canada
| | - Massimo Clerici
- Department of Mental Health and Addiction, Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy; School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Bernardo Maria Dell'Osso
- Department of Biomedical and Clinical Sciences Luigi Sacco and Aldo Ravelli Center for Neurotechnology and Brain Therapeutic, University of Milan, Milano, Italy
| | - Antonio Vita
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Laura Marzetti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Stefano L Sensi
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy
| | - Giorgio Di Lorenzo
- Chair of Psychiatry, Department of Systems Medicine, Tor Vergata University of Rome, Rome, Italy; IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Roger S McIntyre
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Braxia Health, Canadian Centre for Rapid Treatment Excellence (CRTCE), Mississauga, ON, Canada; Brain and Cognition Discovery Foundation, Toronto, ON, Canada
| | - Giovanni Martinotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D'Annunzio, Chieti, Italy; Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK
| |
Collapse
|
5
|
Zhang S, She S, Qiu Y, Li Z, Wu X, Hu H, Zheng W, Huang R, Wu H. Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study. Neuroimage Clin 2023; 39:103468. [PMID: 37473494 PMCID: PMC10372163 DOI: 10.1016/j.nicl.2023.103468] [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: 03/20/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.
Collapse
Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Shenglin She
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Yidan Qiu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zezhi Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaoyan Wu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wei Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
| |
Collapse
|
6
|
Rafique R, Gantassi R, Amin R, Frnda J, Mustapha A, Alshehri AH. Deep fake detection and classification using error-level analysis and deep learning. Sci Rep 2023; 13:7422. [PMID: 37156887 PMCID: PMC10167215 DOI: 10.1038/s41598-023-34629-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 05/04/2023] [Indexed: 05/10/2023] Open
Abstract
Due to the wide availability of easy-to-access content on social media, along with the advanced tools and inexpensive computing infrastructure, has made it very easy for people to produce deep fakes that can cause to spread disinformation and hoaxes. This rapid advancement can cause panic and chaos as anyone can easily create propaganda using these technologies. Hence, a robust system to differentiate between real and fake content has become crucial in this age of social media. This paper proposes an automated method to classify deep fake images by employing Deep Learning and Machine Learning based methodologies. Traditional Machine Learning (ML) based systems employing handcrafted feature extraction fail to capture more complex patterns that are poorly understood or easily represented using simple features. These systems cannot generalize well to unseen data. Moreover, these systems are sensitive to noise or variations in the data, which can reduce their performance. Hence, these problems can limit their usefulness in real-world applications where the data constantly evolves. The proposed framework initially performs an Error Level Analysis of the image to determine if the image has been modified. This image is then supplied to Convolutional Neural Networks for deep feature extraction. The resultant feature vectors are then classified via Support Vector Machines and K-Nearest Neighbors by performing hyper-parameter optimization. The proposed method achieved the highest accuracy of 89.5% via Residual Network and K-Nearest Neighbor. The results prove the efficiency and robustness of the proposed technique; hence, it can be used to detect deep fake images and reduce the potential threat of slander and propaganda.
Collapse
Affiliation(s)
- Rimsha Rafique
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, 47050
| | - Rahma Gantassi
- Department of Electrical Engineering, Chonnam National University, Gwangju, 61186, South Korea
| | - Rashid Amin
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan, 47050.
- Department of Computer Science, University of Chakwal, Chakwal, 48800, Pakistan.
| | - Jaroslav Frnda
- Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026, Zilina, Slovakia
- Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 70800, Ostrava, Czech Republic
| | - Aida Mustapha
- Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, KM1 Jalan Pagoh, 84600, Pagoh, Johor, Malaysia
| | - Asma Hassan Alshehri
- Durma College of Science and Humanities, Shaqra University, Shaqra, 11961, Saudi Arabia
| |
Collapse
|
7
|
Nowicka-Sauer K, Jarmoszewicz K, Molisz A, Sobczak K, Sauer M, Topolski M. Applying Machine Learning to Construct a Model of Risk of Depression in Patients Following Cardiac Surgery with the Use of the SF-12 Survey. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4876. [PMID: 36981783 PMCID: PMC10048966 DOI: 10.3390/ijerph20064876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/28/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Depression is a common problem in patients with cardiovascular diseases. Identifying a risk factor model of depression has been postulated. A model of the risk of depression would provide a better understanding of this disorder in this population. We sought to construct a model of the risk factors of depression in patients following cardiac surgery, with the use of machine learning. METHODS AND MEASURES Two hundred and seventeen patients (65.4% men; mean age 65.14 years) were asked to complete the short form health survey-12 (SF-12v.2), three months after hospital discharge. Those at risk of depression were identified based on the SF-12 mental component summary (MCS). Centroid class principal component analysis (CCPCA) and the classification and regression tree (CART) were used to design a model. RESULTS A risk of depression was identified in 29.03% of patients. The following variables explained 82.53% of the variance in depression risk: vitality, limitation of activities due to emotional problems (role-emotional, RE), New York Heart Association (NYHA) class, and heart failure. Additionally, CART revealed that decreased vitality increased the risk of depression to 45.44% and an RE score > 68.75 increased it to 63.11%. In the group with an RE score < 68.75, the NYHA class increased the risk to 41.85%, and heart failure further increased it to 44.75%. CONCLUSION Assessing fatigue and vitality can help health professionals with identifying patients at risk of depression. In addition, assessing functional status and dimensions of fatigue, as well as the impact of emotional state on daily functioning, can help determine effective intervention options.
Collapse
Affiliation(s)
- Katarzyna Nowicka-Sauer
- Department of Family Medicine, Faculty of Medicine, Medical University of Gdańsk, Dębinki 2 Str., 80-211 Gdańsk, Poland
- Department of Cardiac Surgery, Kashubian Centre for Cardiac and Vascular Diseases, Ceynowa Specialist Hospital, Jagalskiego 10 Str., 84-200 Wejherowo, Poland;
| | - Krzysztof Jarmoszewicz
- Department of Cardiac Surgery, Kashubian Centre for Cardiac and Vascular Diseases, Ceynowa Specialist Hospital, Jagalskiego 10 Str., 84-200 Wejherowo, Poland;
| | - Andrzej Molisz
- Department of Otolaryngology, University Clinical Centre, Medical University of Gdańsk, Smoluchowskiego 17 Str., 80-214 Gdansk, Poland;
| | - Krzysztof Sobczak
- Division of Medical Sociology and Social Pathology, Faculty of Health Sciences, Medical University of Gdańsk, Tuwima 15 Str., 80-210 Gdańsk, Poland;
| | - Marta Sauer
- Radiation Protection Office, University Clinical Centre, Medical University of Gdańsk, Smoluchowskiego 17 Str., 80-214 Gdańsk, Poland;
| | - Mariusz Topolski
- Department of Systems and Computer Networks, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Janiszewskiego 11/17 Str., 50-372 Wroclaw, Poland;
| |
Collapse
|
8
|
A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare (Basel) 2023; 11:healthcare11030285. [PMID: 36766860 PMCID: PMC9914523 DOI: 10.3390/healthcare11030285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.
Collapse
|
9
|
Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [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: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
Collapse
Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
| |
Collapse
|
10
|
Arioz U, Smrke U, Plohl N, Mlakar I. Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models. Diagnostics (Basel) 2022; 12:2683. [PMID: 36359525 PMCID: PMC9689708 DOI: 10.3390/diagnostics12112683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 12/26/2023] Open
Abstract
Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
Collapse
Affiliation(s)
- Umut Arioz
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, The University of Maribor, 2000 Maribor, Slovenia
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| |
Collapse
|