McCarron RM, Shapiro B, Rawles J, Luo J. Depression. Ann Intern Med. 2021;174:ITC65–80.
Malhi GS, Mann JJ. Depression. Lancet. 2018;392(10161):2299–312.
Greenberg PE, Fournier A, Sisitsky T, Pike CT, Kessler RC. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76(2):155–62.
Holvast F, Massoudi B, Voshaar RC, Verhaak PFM. Non-pharmacological treatment for depressed older patients in primary care: a systematic review and meta-analysis. PLoS ONE. 2017;12(9):e0184666.
Article PubMed PubMed Central Google Scholar
Davydow DS, Fenger-Grøn M, Ribe A, Pedersen H, Prior A, Vedsted P, et al. Depression and risk of hospitalisations and rehospitalisations for ambulatory care-sensitive conditions in Denmark: a population-based cohort study. BMJ Open. 2015;5:e009878.
Article PubMed PubMed Central Google Scholar
Park SC, Oh HS, Oh DH, Jung SA, Na KS, Lee HY, et al. Evidence-Based, non-pharmacological treatment guideline for depression in Korea. J Korean Med Sci. 2014;29:12–22.
Article PubMed CAS Google Scholar
Hasin DS, Sarvet AL, Meyers JL, Saha TD, Ruan WJ, Stohl M, Grant BF. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiat. 2018;75:336–46.
Kleine-Budde K, Müller R, Kawohl W, Bramesfeld A, Moock J, Rössler W. The cost of depression—A cost analysis from a large database. J Affect Disord. 2013;147:137–43.
Okumura Y, Higuchi T. Cost of depression among adults in Japan. Prim Care Companion CNS Disord. 2011;13(3):26159.
Zhang L, Chen Y, Yue L, Liu Q, Montgomery W, Zhi L, et al. Medication use patterns, health care resource utilization, and economic burden for patients with major depressive disorder in Beijing, People’s Republic of China. Neuropsychiatr Dis Trea. 2016;20(12):941–9.
Han C, Wang SM, Lee SJ, Patkar AA, Masand PS, Pae CU. Second-generation antipsychotics in the treatment of major depressive disorder: current evidence. Expert Rev Neurother. 2013;13(7):851–70.
Article PubMed CAS Google Scholar
Valenstein M. Keeping our eyes on STAR*D. AJP. 2006;163:1484–6.
Wiles N, Taylor A, Turner N, Barnes M, Campbell J, Lewis G, Morrison J, Peters TJ, Thomas L, Turner K, et al. Management of treatment-resistant depression in primary care: a mixed-methods study. Br J Gen Pr. 2018;68:e673–81.
Rege S, Sura S, Aparasu RR. Atypical antipsychotic prescribing in elderly patients with depression. Res Social Adm Pharm. 2018;14:645–52.
EMA, Questions and answers on Seroquel XR and associated names (50, 150, 200, 300 and 400 mg prolonged-release tablets containing quetiapine), EMA Website. 2010. https://www.ema.europa.eu/en/documents/referral/questions-answers-seroquel-xr-associated-names-50-150-200-300-400-mg-prolonged-release-tablets_en.pdf. Accessed 27 Sep 2019.
Cleare A, Pariante CM, Young AH, et al. Evidence-based guidelines for treating depressive disorders with antidepressants: a revision of the 2008 British Association for Psychopharmacology guidelines. J Psychopharmacol. 2015;29:459–525.
Article PubMed CAS Google Scholar
Kennedy SH, Lam RW, McIntyre RS, et al. Canadian network for mood and anxiety treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: section 3 Pharmacological treatments. Can J Psychiatry. 2016;61:540–60.
Article PubMed PubMed Central Google Scholar
Hiemke C, Bergemann N, Clement HW, Conca A, Deckert J, Domschke K, Eckermann G, Egberts K, Gerlach M, Greiner C, Gründer G, Haen E, Havemann-Reinecke U, Hefner G, Helmer R, Janssen G, Jaquenoud E, Laux G, Messer T, Mössner R, Müller MJ, Paulzen M, Pfuhlmann B, Riederer P, Saria A, Schoppek B, Schoretsanitis G, Schwarz M, Gracia MS, Stegmann B, Steimer W, Stingl JC, Uhr M, Ulrich S, Unterecker S, Waschgler R, Zernig G, Zurek G, Baumann P. Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: update 2017. Pharmacopsychiatry. 2018;51(1–02):e1. https://doi.org/10.1055/s-0037-1600991. (Epub 2018 Feb 1. Erratum for: Pharmacopsychiatry. 2018 Jan;51(1–02):9–62).
Article PubMed CAS Google Scholar
Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, Mattonen SA, El Naga I. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185–202.
Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence and diabetes technology: a review. Metabolism. 2021;124:154872.
Article PubMed CAS Google Scholar
Rani P, Kotwal S, Manhas J, Sharma V, Sharma S. Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: methodologies, challenges, and developments. Arch Comput Methods. 2021;29:1–37.
Huang X, Yu Z, Wei X, Shi J, Wang Y, Wang Z, Chen J, Bu S, Li L, Gao F, Zhang J, Xu A. Prediction of vancomycin dose on high-dimensional data using machine learning techniques. Expert Rev Clin Pharmacol. 2021;14(6):761–71.
Article PubMed CAS Google Scholar
Liu Y, Chen J, You Y, Xu A, Li P, Wang Y, Sun J, Yu Z, Gao F, Zhang J. An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set. Comput Biol Med. 2021;131:104242.
Article PubMed CAS Google Scholar
National Health Commission of the People’s Republic of China. Code for Diagnosis and Treatment of Mental Disorders 2020 [M]. The National Health Commission of the People’s Republic of China. 2020;5:164.
Chen H, Ma Y, Hong N, Wang H, Su L, Liu C, He J, Jiang H, Long Y, Zhu W. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak. 2021;21(Suppl 2):126.
Article PubMed PubMed Central Google Scholar
Powers DMW. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv. Preprint posted online. Accessed 11 Oct 2020.
Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L, Fava M. Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: Implications for clinical practice. Am J Psychiatry. 2006;163(1):28–40.
National Collaborating Centre for Mental Health (UK). Depression: The Treatment and Management of Depression in Adults (Updated Edition). Leicester (UK): British Psychological Society. 2010
Seshadri A, Wermers ML, Habermann TJ, et al. Long-term efficacy and tolerability of adjunctive aripiprazole for major depressive disorder: systematic review and meta-analysis. Prim Care Companion CNS Disord. 2021;23(4):34898.
You W, Widmer N, De Micheli G. Example-based support vector machine for drug concentration analysis. In: Conf Proc IEEE Eng Med Biol Soc. 2011, 153–157.
Ludden TM. Population pharmacokinetics. J Clin Pharmacol. 1988;28:1059–63.
Article PubMed CAS Google Scholar
Johansson ÅM, Ueckert S, Plan EL, Hooker AC, Karlsson MO. Evaluation of bias, precision, robustness and runtime for estimation methods in NONMEM 7. J Pharmacokinet Pharmacodyn. 2014;41:223–38.
Article PubMed CAS Google Scholar
Sibieude E, Khandelwal A, Girard P, Hesthaven JS, Terranova N. Population pharmacokinetic model selection assisted by machine learning. J Pharmacokinet Pharmacodyn. 2021. https://doi.org/10.1007/s10928-021-09793-6.
Article PubMed PubMed Central Google Scholar
Huang X, Yu Z, Bu S, Lin Z, Hao X, He W, et al. An ensemble model for prediction of vancomycin trough concentrations in pediatric patients. Drug Des Devel Ther. 2021;15:1549–59.
Article PubMed PubMed Central Google Scholar
Poynton MR, Choi BM, Kim YM, Park IS, Noh GJ, Hong SO, et al. Machine learning methods applied to pharmacokinetic modelling of remifentanil in healthy volunteers: a multi-method comparison. J Int Med Res. 2009;37:1680–91.
Article PubMed CAS Google Scholar
Shatte A, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49:1426–48.
Meng HY, Jin WL, Yan CK, Yang H. The application of machine learning techniques in clinical drug therapy. Curr Comput Aided Drug Des. 2019;15:111–9.
Article PubMed CAS Google Scholar
Jovanović M, et al. Application of counter-propagation artificial neural networks in prediction of topiramate concentration in patients with epilepsy. J Pharm Pharm Sci. 2015;18:856–62. https://doi.org/10.18433/j33031.
Tang J, et al. Application of machine-learning models to predict tacrolimus stable dose in renal transplant recipients. Sci Rep. 2017;7:42192.
Article PubMed PubMed Central CAS Google Scholar
Liu R, Li X, Zhang W, Zhou HH. Comparison of nine statistical model based warfarin pharmacogenetic dosing algorithms using the racially diverse international warfarin pharmacogenetic consortium cohort database. PLoS ONE. 2015;10:e0135784.
Article PubMed PubMed Central Google Scholar
Ma Z, Wang P, Gao Z, Wang R, Khalighi K. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS ONE. 2018;13:e0205872.
Article PubMed PubMed Central Google Scholar
Roche-Lima A, et al. Machine learning algorithm for predicting warfarin dose in Caribbean hispanics using pharmacogenetic data. Front Pharmacol. 2020;10:1550.
Comments (0)