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Depression

Prediction of depressive symptoms onset and long-term trajectories in home-based older adults using machine learning techniques

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Pages 8-17 | Received 19 Jul 2021, Accepted 11 Jan 2022, Published online: 04 Feb 2022

References

  • Aichele, S., Rabbitt, P., & Ghisletta, P. (2019). Illness and intelligence are comparatively strong predictors of individual differences in depressive symptoms following middle age. Aging & Mental Health, 23(1), 122–131. https://doi.org/10.1080/13607863.2017.1394440
  • Andreescu, C., Chang, C., Mulsant, B. H., & Ganguli, M. (2008). Twelve-year depressive symptom trajectories and their predictors in a community sample of older adults. International Psychogeriatrics, 20(02), 221–236. https://doi.org/10.1017/S1041610207006667
  • Ay, B., Yildirim, O., Talo, M., Baloglu, U. B., Aydin, G., Puthankattil, S. D., & Acharya, U. R. (2019). Automated depression detection using deep representation and sequence learning with EEG signals. Journal of Medical Systems, 43(7), 205. https://doi.org/10.1007/s10916-019-1345-y
  • Aziz, R., & Steffens, D. C. (2013). What are the causes of late-life depression? The Psychiatric Clinics of North America, 36(4), 497–516. https://doi.org/10.1016/j.psc.2013.08.001
  • Beard, J. R., Tracy, M., Vlahov, D., & Galea, S. (2008). Trajectory and socioeconomic predictors of depression in a prospective study of residents of New York City. Annals of Epidemiology, 18(3), 235–243. https://doi.org/10.1016/j.annepidem.2007.10.004
  • Bhagwat, N., Viviano, J. D., Voineskos, A. N., Chakravarty, M. M., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using longitudinal data. PLoS Computational Biology, 14(9), e1006376. https://doi.org/10.1371/journal.pcbi.1006376
  • Bilkis, M. S., Islam, M., Zaman, F., Zinia, S. N., & Rahman, M. (2020). Lifestyle and depression in urban elderly of selected district of Bangladesh. Mymensingh Medical Journal: MMJ, 29(1), 177–182.
  • Byers, A. L., Vittinghoff, E., Lui, L.-Y., Hoang, T., Blazer, D. G., Covinsky, K. E., Ensrud, K. E., Cauley, J. A., Hillier, T. A., Fredman, L., & Yaffe, K. (2012). Twenty-year depressive trajectories among older women. Archives of General Psychiatry, 69(10), 1073. https://doi.org/10.1001/archgenpsychiatry.2012.43
  • Bzdok, D., Altman, N., & Krzywinski, M. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233–234.
  • Carvalho, A. F., Köhler, C. A., McIntyre, R. S., Knöchel, C., Brunoni, A. R., Thase, M. E., Quevedo, J., Fernandes, B. S., & Berk, M. (2015). Peripheral vascular endothelial growth factor as a novel depression biomarker: A meta-analysis. Psychoneuroendocrinology, 62, 18–26. https://doi.org/10.1016/j.psyneuen.2015.07.002
  • Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016). Cross-trial prediction of treatment outcome in depression: A machine learning approach. The Lancet Psychiatry, 3(3), 243–250. https://doi.org/10.1016/S2215-0366(15)00471-X
  • Chen, H., & Mui, A. (2014). Factorial validity of the Center for Epidemiologic Studies Depression Scale short form in older population in China. International Psychogeriatrics, 26(1), 49–49. https://doi.org/10.1017/S1041610213001701
  • Chen, Y.-Y., Wong, G. H. Y., Lum, T. Y., Lou, V. W. Q., Ho, A. H. Y., Luo, H., & Tong, T. L. W. (2016). Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders. Aging & Mental Health, 20(4), 423–431. https://doi.org/10.1080/13607863.2015.1018867
  • Costello, D. M., Swendsen, J., Rose, J. S., & Dierker, L. C. (2008). Risk and protective factors associated with trajectories of depressed mood from adolescence to early adulthood. Journal of Consulting and Clinical Psychology, 76(2), 173–183. https://doi.org/10.1037/0022-006X.76.2.173
  • Deng, P., Gan, W., Liu, W. F., Xie, T., Peng, G. G., & Si-Jian, L. I. (2008). The depression conditions among old people in some community and the influential factors. Journal of Nursing,15(11), 82-84. https://doi.org/10.16460/j.issn1008-9969.2008.11.043.
  • Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A., Schoevers, R. A., Hemert, A. V., Penninx, B., & Schmaal, L. (2018). Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: A machine learning approach. Translational Psychiatry, 8(1), 241. https://doi.org/10.1038/s41398-018-0289-1
  • Facal, D., Valladares-Rodriguez, S., Lojo-Seoane, C., Pereiro, A. X., Anido-Rifon, L., & Juncos-Rabadán, O. (2019). Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia. International Journal of Geriatric Psychiatry, 34(7), 941–949. https://doi.org/10.1002/gps.5090
  • Ghafoor, Y., Huang, Y. P., & Liu, S. I. (2015). An intelligent approach to discovering common symptoms among depressed patients. Soft Computing, 19(4), 819–827. https://doi.org/10.1007/s00500-014-1408-4
  • Grzenda, A., Speier, W., Siddarth, P., Pant, A., Krause-Sorio, B., Narr, K., & Lavretsky, H. (2021). Machine learning prediction of treatment outcome in late-life depression. Frontiers in Psychiatry, 12, 738494. https://doi.org/10.3389/fpsyt.2021.738494
  • Hajek, A., Brettschneider, C., Eisele, M., Luehmann, D., & Mamone, S. (2017). Disentangling the complex relation of disability and depressive symptoms in old age - Findings of a multicenter prospective cohort study in Germany. International Psychogeriatrics, 29(6), 885–895.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning data mining, inference, and prediction (2nd ed.). Springer.
  • He, M., Ma, J., Ren, Z., Zhou, G., Gong, P., Liu, M., Yang, X., Xiong, W., Wang, Q., Liu, H., & Zhang, X. (2019). Association between activities of daily living disability and depression symptoms of middle-aged and older Chinese adults and their spouses: A community based study. Journal of Affective Disorders, 242, 135–142. https://doi.org/10.1016/j.jad.2018.08.060
  • Houtven, C., & Norton, E. C. (2004). Informal care and health care use of older adults. Journal of Health Economics, 23(6), 1159–1180.
  • Hsu, H. C. (2012). Group-based trajectories of depressive symptoms and the predictors in the older population. International Journal of Geriatric Psychiatry, 27(8), 854–862. https://doi.org/10.1002/gps.2796
  • Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., & Jaffe, M. W. (1963). Studies of illness in the aged. The index of ADL: A standardized measure of biological and psychosocial function. JAMA, 185, 914–919. https://doi.org/10.1001/jama.1963.03060120024016
  • Kaup, A. R., Byers, A. L., Fa Lvey, C., Simonsick, E. M., Satterfield, S., Ayonayon, H. N., Smagula, S. F., Rubin, S. M., & Yaffe, K. (2016). Trajectories of depressive symptoms in older adults and risk of dementia. JAMA Psychiatry, 73(5), 525. https://doi.org/10.1001/jamapsychiatry.2016.0004
  • Kessler, R. C., & Bromet, E. J. (2013). The epidemiology of depression across cultures. Annual Review of Public Health, 34(1), 119–138.
  • Kessler, R. C., van Loo, H. M., Wardenaar, K. J., Bossarte, R. M., Brenner, L. A., Cai, T., Ebert, D. D., Hwang, I., Li, J., de Jonge, P., Nierenberg, A. A., Petukhova, M. V., Rosellini, A. J., Sampson, N. A., Schoevers, R. A., Wilcox, M. A., & Zaslavsky, A. M. (2016). Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry, 21(10), 1366–1371. https://doi.org/10.1038/mp.2015.198
  • Koh, H. K., & Parekh, A. K. (2018). Toward a United States of Health: Implications of Understanding the US Burden of Disease. JAMA, 319(14), 1438–1440. https://doi.org/10.1001/jama.2018.0157
  • Krstajic, D., Buturovic, L. J., Leahy, D. E., & Thomas, S. (2014). Cross-validation pitfalls when selecting and assessing regression and classification models. Journal of Cheminformatics, 6(1), 10. https://doi.org/10.1186/1758-2946-6-10
  • Kuchibhatla, M. N., Fillenbaum, G. G., Hybels, C. F., & Dan, G. B. (2012). Trajectory classes of depressive symptoms in a community sample of older adults. Acta Psychiatrica Scandinavica, 125(6), 492–501.
  • Kuo, S. Y., Lin, K. M., Chen, C. Y., Chuang, Y. L., & Chen, W. J. (2011). Depression trajectories and obesity among the elderly in Taiwan. Psychological Medicine, 41(8), 1665–1676.
  • Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist, 9(3), 179–186. https://doi.org/10.1093/geront/9.3_Part_1.179
  • Lei, X., Hu, Y., McArdle, J. J., Smith, J. P., & Zhao, Y. (2012). Gender differences in cognition among older adults in China. The Journal of Human Resources, 47(4), 951–971. https://doi.org/10.3368/jhr.47.4.951
  • Lei, X., Smith, J. P., Sun, X., & Zhao, Y. (2014). Gender differences in cognition in China and reasons for change over time: Evidence from CHARLS. Journal of the Economics of Ageing, 4, 46–55.
  • Lgb, A., Khcb, C., & Jsjab, D. (2020). Association between depression and disease-specific treatment. Journal of Affective Disorders, 260, 124–130.
  • Li, X., La, R., Wang, Y., Niu, J., Zeng, S., Sun, S., & Zhu, J. (2019). EEG-based mild depression recognition using convolutional neural network. Medical & Biological Engineering & Computing, 57(6), 1341–1352. https://doi.org/10.1007/s11517-019-01959-2
  • Liang, J., Xu, X., Qui? Ones, A. R., Bennett, J. M., & Ye, W. (2011). Multiple trajectories of depressive symptoms in middle and late life: Racial/ethnic variations. Psychology and Aging, 26(4), 761–777.
  • Librenza-Garcia, D., Passos, I. C., Feiten, J. G., Lotufo, P. A., & Brunoni, A. R. (2020). Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: An analysis of the ELSA-Brasil study. Psychological Medicine, 51(16), 2895–2903.
  • Liu, S., Rovine, M. J., & Molenaar, P. C. M. (2012). Selecting a linear mixed model for longitudinal data: Repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychological Methods, 17(1), 15–30. https://doi.org/10.1037/a0026971
  • Luoma, I., Korhonen, M., Salmelin, R. K., Helminen, M., & Tamminen, T. (2015). Long-term trajectories of maternal depressive symptoms and their antenatal predictors. Journal of Affective Disorders, 170, 30–38.
  • Mansori, K., Shiravand, N., Shadmani, F. K., Moradi, Y., Allahmoradi, M., Ranjbaran, M., Ahmadi, S., Farahani, A., Samii, K., & Valipour, M. (2019). Association between depression with glycemic control and its complications in type 2 diabetes. Diabetes & Metabolic Syndrome Clinical Research & Reviews, 13(2). 1555–1560.
  • Mirza, S. S., Wolters, F. J., Swanson, S. A., Koudstaal, P. J., Hofman, A., Tiemeier, H., & Ikram, M. A. (2016). 10-year trajectories of depressive symptoms and risk of dementia: A population-based study. The Lancet: Psychiatry, 3(7), 628–635. https://doi.org/10.1016/S2215-0366(16)00097-3
  • Montagnier, D., Dartigues, J.-F., Rouillon, F., Pérès, K., Falissard, B., & Onen, F. (2014). Ageing and trajectories of depressive symptoms in community-dwelling men and women. International Journal of Geriatric Psychiatry, 29(7), 720–729. https://doi.org/10.1002/gps.4054
  • Musliner, K. L., Munk-Olsen, T., Eaton, W. W., & Zandi, P. P. (2016). Heterogeneity in long-term trajectories of depressive symptoms: Patterns, predictors and outcomes. Journal of Affective Disorders, 192, 199–211. https://doi.org/10.1016/j.jad.2015.12.030
  • Na, K. S., Cho, S. E., Geem, Z. W., & Kim, Y. K. (2020). Predicting future onset of depression among community dwelling adults in the Republic of Korea using a machine learning algorithm. Neuroscience Letters, 721, 134804.
  • Ouyang, P., & Sun, W. J. (2019). Depression and sleep duration: Findings from middle-aged and elderly people in China. Public Health, 166, 148–154. https://doi.org/10.1016/j.puhe.2018.10.007
  • Pereira, L. P., K., Hler, C. A., Stubbs, B., Miskowiak, K. W., Morris, G., Freitas, B. D., Thompson, T., Fernandes, B. S., Brunoni, A. R., & Maes, M. (2018). Imaging genetics paradigms in depression research: Systematic review and meta-analysis. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 86, 102–113.
  • Philip, G., Seelig, A. D., Jacobson, I. G., Boyko, E. J., Hooper, T. I., Gackstetter, G. D., Ulmer, C. S., Smith, T. C., & Team, M. C. S. (2013). Predeployment sleep duration and insomnia symptoms as risk factors for new-onset mental health disorders following military deployment. Sleep, 36(7), 1009–1018.
  • Qiu, P., Zeng, M., Kuang, W., Meng, S. S., Cai, Y., Wang, H., & Wan, Y. (2020). Heterogeneity in the dynamic change of cognitive function among older Chinese people: A growth mixture model. International Journal of Geriatric Psychiatry, 35(10), 1123–1133. https://doi.org/10.1002/gps.5334
  • Richardson, R., Westley, T., Gariepy, G., Austin, N., & Nandi, A. (2015). Neighborhood socioeconomic conditions and depression: A systematic review and meta-analysis. Social Psychiatry and Psychiatric Epidemiology, 50(11), 1641–1656. https://doi.org/10.1007/s00127-015-1092-4
  • Singh-Manoux, A., Dugravot, A., Fournier, A., Abell, J., Ebmeier, K., Kivimaki, M., & Sabia, S. (2017). Trajectories of depressive symptoms before diagnosis of dementia: A 28-year follow-up study. JAMA Psychiatry, 74(7), 712–718. https://doi.org/10.1001/jamapsychiatry.2017.0660
  • Skapinakis, P., Welch, S., Lewis, G., Singleton, N., Araya, R., & Weich, S. (2006). Socio-economic position and common mental disorders. British Journal of Psychiatry, 189(2), 109–117. https://doi.org/10.1192/bjp.bp.105.014449
  • Spijker, J., de Graaf, R., Bijl, R. V., Beekman, A. T. F., Ormel, J., & Nolen, W. A. (2004). Determinants of persistence of major depressive episodes in the general population. Results from the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Journal of Affective Disorders, 81(3), 231–240. https://doi.org/10.1016/j.jad.2003.08.005
  • Stuart, S. A., Hinchcliffe, J. K., & Robinson, E. (2019). Evidence that neuropsychological deficits following early life adversity may underlie vulnerability to depression. Neuropsychopharmacology, 44, 1623–1630.
  • Studerus, E., Ramyead, A., & Riecher-Rossler, A. (2017). Prediction of transition to psychosis in patients with a clinical high risk for psychosis: A systematic review of methodology and reporting. Psychological Medicine, 47(7), 1163–1178. https://doi.org/10.1017/S0033291716003494
  • Suhara, Y., Xu, Y., Pentland, A. S. & Acm, (2017 DeepMood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In WWW ‘17: Proceedings of the 26th International Conference on World Wide Web, 715–724. ACM. https://doi.org/10.1145/3038912.3052676
  • Suykens, J. A. K., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9(3), 293–300. https://doi.org/10.1023/A:1018628609742
  • Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning. Springer.
  • Tommasin, S., Cocozza, S., Taloni, A., Gianni, C., Petsas, N., Pontillo, G., Petracca, M., Ruggieri, S., De Giglio, L., Pozzilli, C., Brunetti, A., & Pantano, P. (2021). Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. Journal of Neurology, 268(12), 4834–4845. https://doi.org/10.1007/s00415-021-10605-7
  • Unsar, S., Dindar, I., & Kurt, S. (2015). Activities of daily living, quality of life, social support and depression levels of elderly individuals in Turkish society. Journal of the Pakistan Medical Association, 65(6), 642–646.
  • Vapnik, V. N. (2000). The nature of statistic learning theory. Springer.
  • Vinkers, D. J., Jacobijn, G., Stek, M. L., Westendorp, R. G. J., & van der Mast, R. C. (2018). Temporal relation between depression and cognitive impairment in old age: Prospective population based study. BMJ (Clinical Research ed.), 329, 881.
  • Wen, Z., & Chan, M. F. (2015). Exploring risk factors for depression among older men residing in Macau. Journal of Clinical Nursing, 20(17–18), 2645–2654.
  • WHO. (2017). Depression and other common mental disorders: Global health estimates. No. WHO/MSD/MER/2017.2. https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf
  • Wickrama, K., Lee, T. K., O’Neal, C. W., & Lorenz, F. (2016). Higher-order growth curves and mixture modeling with Mplus: A practical guide. Routledge.
  • Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019). Gradient boosting decision tree-based method for predicting interactions between target genes and drugs. Frontiers in Genetics, 10, 459. https://doi.org/10.3389/fgene.2019.00459
  • Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
  • Yaroslavsky, I., Pettit, J. W., Lewinsohn, P. M., Seeley, J. R., & Roberts, R. E. (2013). Heterogeneous trajectories of depressive symptoms: Adolescent predictors and adult outcomes. Journal of Affective Disorders, 148(2–3), 391–399.
  • Zhao, Y., Hu, Y., Smith, J. P., Strauss, J., & Yang, G. (2014). Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). International Journal of Epidemiology, 43(1), 61–68. https://doi.org/10.1093/ije/dys203

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