A monitoring framework for health care processes using Generalized Additive Models and Auto-Encoders
Tài liệu tham khảo
Li, 2018, Risk-adjusted monitoring of surgical performance, PloS One, 13, 10.1371/journal.pone.0200915
Valente, 2021, A new approach for interpretability and reliability in clinical risk prediction: acute coronary syndrome scenario, Artif Intell Med, 117, 10.1016/j.artmed.2021.102113
Motwani, 2022, Ubiquitous and smart healthcare monitoring frameworks based on machine learning: a comprehensive review, Artif Intell Med, 134, 10.1016/j.artmed.2022.102431
Scagliarini, 2021, Comparison of control charts for Poisson count data in health-care monitoring, Applied Stochastic Models in Business and Industry, 37, 139, 10.1002/asmb.2560
Gandy, 2010, Risk-adjusted monitoring of time to event, Biometrika, 97, 375, 10.1093/biomet/asq004
Baker, 2020, Use of optimised dual statistical process control charts for early detection of surgical site infection outbreaks, BMJ Qual Saf, 29, 517, 10.1136/bmjqs-2019-010586
You, 2020, Early detection of high disease activity in juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores, Biom J, 62, 1343, 10.1002/bimj.201900127
Ashraf, 2021, Online disease risk monitoring using DEWMA control chart, Expert Systems with Applications, 180, 10.1016/j.eswa.2021.115059
Lai, 2021, A risk-adjusted approach to monitoring surgery for survival outcomes based on a weighted score test, Computers & Industrial Engineering, 160, 10.1016/j.cie.2021.107568
Novick, 2006, Direct comparison of risk-adjusted and non–risk-adjusted CUSUM analyses of coronary artery bypass surgery outcomes, J Thorac Cardiovasc Surg, 132, 386, 10.1016/j.jtcvs.2006.02.053
Erfanian, 2021, A new approach for monitoring healthcare performance using generalized additive profiles, Journal of Statistical Computation and Simulation, 91, 167, 10.1080/00949655.2020.1807981
Steiner, 2000, Monitoring surgical performance using risk-adjusted cumulative sum charts, Biostatistics, 1, 441, 10.1093/biostatistics/1.4.441
Gombay, 2011, Monitoring binary outcomes using risk-adjusted charts: a comparative study, Stat Med, 30, 2815, 10.1002/sim.4305
Knoth, 2019, Risk-adjusted CUSUM charts under model error, Stat Med, 38, 2206, 10.1002/sim.8104
Yeganeh, 2023, Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance, Neural Computing and Applications, 35, 10677, 10.1007/s00521-023-08257-x
Liu, 2018, Online profile monitoring for surgical outcomes using a weighted score test, Journal of Quality Technology, 50, 88, 10.1080/00224065.2018.1404329
Rasouli, 2022, Monitoring multistage multivariate therapeutic processes using risk-adjusted model-based group multivariate EWMA control chart, Quality and Reliability Engineering International, 38, 2445, 10.1002/qre.3085
Woodall, 2015, The monitoring and improvement of surgical-outcome quality, Journal of Quality Technology, 47, 383, 10.1080/00224065.2015.11918141
Sachlas, 2019, Risk-adjusted control charts: theory, methods, and applications in health, Statistics in Biosciences, 11, 630, 10.1007/s12561-019-09257-z
Chung, 2020, Medical device active surveillance of spontaneous reports: a literature review of signal detection methods, Pharmacoepidemiol Drug Saf, 29, 369, 10.1002/pds.4980
Asif, 2022, Accelerated failure time model based risk adjusted MA-EWMA control chart, Communications in Statistics - Simulation and Computation, 1, 10.1080/03610918.2022.2155315
Lai, 2023, Generalized likelihood ratio based risk-adjusted control chart for zero-inflated Poisson process, Quality and Reliability Engineering International, 39, 363, 10.1002/qre.3244
Mohammadian, 2016, Phase-I risk-adjusted geometric control charts to monitor health-care systems, Quality and Reliability Engineering International, 32, 19, 10.1002/qre.1722
Grigg, 2007, A simple risk-adjusted exponentially weighted moving average, J Am Stat Assoc, 102, 140, 10.1198/016214506000001121
Huang, 2023, Monitoring the performance of a dedicated weaning unit using risk-adjusted control charts for the weaning rate in prolonged mechanical ventilation, J Formos Med Assoc, 122, 880, 10.1016/j.jfma.2023.04.021
Giani, 2022, Multidimensional evaluation of the learning curve for laparoscopic complete mesocolic excision for right colon cancer: a risk-adjusted cumulative summation analysis, Colorectal Dis, 24, 577, 10.1111/codi.16075
Biswas, 2008, A risk-adjusted CUSUM in continuous time based on the Cox model, Stat Med, 27, 3382, 10.1002/sim.3216
Ali, 2020, On the effect of estimation error for the risk-adjusted charts, Complexity, 2020, 6258010, 10.1155/2020/6258010
Chukhrova, 2020, Monitoring of high-yield and periodical processes in health care, Health Care Manag Sci, 23, 619, 10.1007/s10729-020-09514-4
Wittenberg, 2022, Modeling the patient mix for risk-adjusted CUSUM charts, Stat Methods Med Res, 31, 779, 10.1177/09622802211053205
Barrio, 2013, Use of generalised additive models to categorise continuous variables in clinical prediction, BMC Med Res Methodol, 13, 83, 10.1186/1471-2288-13-83
Marafino, 2018, Accurate and interpretable intensive care risk adjustment for fused clinical data with generalized additive models, AMIA Jt Summits Transl Sci Proc, 2017, 166
Ye, 2022, Evaluation of different factors on metal leaching from nickel tailings using generalized additive model (GAM), Ecotoxicol Environ Saf, 236, 10.1016/j.ecoenv.2022.113488
Wong, 2023, Quantification of COVID-19 impacts on NO2 and O3: systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods, Atmos Environ, 301, 10.1016/j.atmosenv.2023.119677
Lee, 2019, Process monitoring using variational autoencoder for high-dimensional nonlinear processes, Eng Appl Artif Intel, 83, 13, 10.1016/j.engappai.2019.04.013
Chen, 2020, Monitoring of complex profiles based on deep stacked denoising autoencoders, Computers & Industrial Engineering, 143, 10.1016/j.cie.2020.106402
Cacciarelli, 2022, A novel fault detection and diagnosis approach based on orthogonal autoencoders, Computers & Chemical Engineering, 163, 10.1016/j.compchemeng.2022.107853
Biegel, 2022, Deep learning for multivariate statistical in-process control in discrete manufacturing: a case study in a sheet metal forming process, Procedia CIRP, 107, 422, 10.1016/j.procir.2022.05.002
Johannssen, 2022, The performance of the hypergeometric np chart with estimated parameter, European Journal of Operational Research, 296, 873, 10.1016/j.ejor.2021.06.056
Munir, 2023, Effect of measurement uncertainty on combined quality control charts, Computers & Industrial Engineering, 175, 10.1016/j.cie.2022.108900
Yao, 2023, Phase I control chart for individual autocorrelated data: application to prescription opioid monitoring, Journal of Quality Technology, 1
Montgomery, 2020
Yeganeh, 2023, A network surveillance approach using machine learning based control charts, Expert Systems with Applications, 219, 10.1016/j.eswa.2023.119660
Ouarda, 2018, Introduction of the GAM model for regional low-flow frequency analysis at ungauged basins and comparison with commonly used approaches, Environ Model Software, 109, 256, 10.1016/j.envsoft.2018.08.031
Salehi, 2018, Five-year recurrence rate and the predictors following stroke in the Mashhad stroke incidence study: a population-based cohort study of stroke in the Middle East, Neuroepidemiology, 50, 18, 10.1159/000485509
Azarpazhooh, 2010, Excessive incidence of stroke in Iran, Stroke, 41, e3, 10.1161/STROKEAHA.109.559708
Farzadfard, 2019, Long-term disability after stroke in Iran: evidence from the Mashhad Stroke Incidence Study, Int J Stroke, 14, 44, 10.1177/1747493018789839
Morovatdar, 2019, Socioeconomic status and long-term stroke mortality, recurrence and disability in Iran: the Mashhad stroke incidence study, Neuroepidemiology, 53, 27, 10.1159/000494885
Leoni, 2015, The effect of the autocorrelation on the performance of the T2 chart, European Journal of Operational Research, 247, 155, 10.1016/j.ejor.2015.05.077
Dargopatil, 2019, New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process, Quality and Reliability Engineering International, 35, 30, 10.1002/qre.2378
Williams, 2007, Statistical monitoring of nonlinear product and process quality profiles, Quality and Reliability Engineering International, 23, 925, 10.1002/qre.858
Chang, 2010, Statistical process control for monitoring non-linear profiles using wavelet filtering and B-spline approximation, International Journal of Production Research, 48, 1049, 10.1080/00207540802454799
Liu, 2018, A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis, Math Probl Eng, 2018, 5105709
Tang, 2019, Motor imagery EEG recognition with KNN-based smooth auto-encoder, Artif Intell Med, 101, 10.1016/j.artmed.2019.101747
Yu, 2019, Stacked denoising autoencoder-based feature learning for out-of-control source recognition in multivariate manufacturing process, Quality and Reliability Engineering International, 35, 204, 10.1002/qre.2392
Dittrich, 2021, Variable selection for monitoring sickness behavior in lactating dairy cattle with the application of control charts, J Dairy Sci, 104, 7956, 10.3168/jds.2020-19680
Ding, 2021, A new risk-adjusted EWMA control chart based on survival time for monitoring surgical outcome quality, Quality and Reliability Engineering International, 37, 1650, 10.1002/qre.2818
Cheng, 2016, Diagnosing the variance shifts signal in multivariate process control using ensemble classifiers, Journal of the Chinese Institute of Engineers, 39, 64, 10.1080/02533839.2015.1073662
Zan, 2020, Control chart pattern recognition using the convolutional neural network, J Intell Manuf, 31, 703, 10.1007/s10845-019-01473-0
Montesinos López, 2022, Fundamentals of artificial neural networks and deep learning, 379
Cheng, 2011, Using neural networks to detect the bivariate process variance shifts pattern, Computers & Industrial Engineering, 60, 269, 10.1016/j.cie.2010.11.009
Yeganeh, 2021, Monitoring linear profiles using Artificial Neural Networks with run rules, Expert Systems with Applications, 168, 10.1016/j.eswa.2020.114237
Williams, 2006, Distribution of Hotelling’s T2 statistic based on the successive differences estimator, Journal of Quality Technology, 38, 217, 10.1080/00224065.2006.11918611
Fatt Gan, 2020, Quicker detection risk-adjusted cumulative sum charting procedures, Stat Med, 39, 875, 10.1002/sim.8448
Wittenberg, 2018, A simple signaling rule for variable life-adjusted display derived from an equivalent risk-adjusted CUSUM chart, Stat Med, 37, 2455, 10.1002/sim.7647
Huwang, 2014, Monitoring general linear profiles using simultaneous confidence sets schemes, Computers & Industrial Engineering, 68, 1, 10.1016/j.cie.2013.11.014
Tran, 2021, One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs, Journal of Manufacturing Processes, 69, 273, 10.1016/j.jmapro.2021.07.031
Assareh, 2014, Estimation of the time of a linear trend in monitoring survival time, Health Services and Outcomes Research Methodology, 14, 15, 10.1007/s10742-014-0115-z
Zhou, 2023, Cyclic alternating pattern in non-rapid eye movement sleep in patients with vestibular migraine, Sleep Med, 101, 485, 10.1016/j.sleep.2022.11.034
Ghiasabadi, 2013, Identifying change point of a non-random pattern on control chart using artificial neural networks, The International Journal of Advanced Manufacturing Technology, 67, 1623, 10.1007/s00170-012-4595-0
Yeganeh, 2021, An ANN-based ensemble model for change point estimation in control charts, Appl Soft Comput, 110, 10.1016/j.asoc.2021.107604
Yue, 2017, Multivariate nonparametric control chart with variable sampling interval, App Math Model, 52, 603, 10.1016/j.apm.2017.08.005
Ugaz, 2017, Adaptive EWMA control charts with time-varying smoothing parameter, The International Journal of Advanced Manufacturing Technology, 93, 3847, 10.1007/s00170-017-0792-1
Kosztyán, 2018, Risk-based X-bar chart with variable sample size and sampling interval, Computers & Industrial Engineering, 120, 308, 10.1016/j.cie.2018.04.052
Yeganeh, 2023, Combination of sequential sampling technique with GLR control charts for monitoring linear profiles based on the random explanatory variables, Mathematics, 11, 1683, 10.3390/math11071683