Abstract
Processes described by indirectly observed data naturally arise in applications, such as telehealth systems. The available data can be used to predict the characteristics of interest, which form a process to be monitored. Its randomness is largely related to the classification (diagnosis) errors. To minimize them, one can use ensembles of predictors or try to benefit from the availability of heterogeneous sources of data. However, these techniques require certain modifications to the control charts, which we discuss in this paper. We consider three methods of classification: classical—based on the full set of attributes, and two combined—based on the number of positive evaluations yielded by an ensemble of inter-correlated classifiers. For monitoring the results of classification, we use a moving average control chart for serially dependent binary data. The application of the proposed procedure is illustrated with a real example of the monitoring of patients suffering from bipolar disorder. This monitoring procedure aims to detect a possible change in a patient’s state of health.
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Acknowledgements
The study was submitted to the Office for Registration of Medicinal Products, Medical Devices and Biocidal Products in accordance with Polish law. This work was partially financed from EU funds (Regional Operational Program for Mazovia)—a project entitled “Smartphone-based diagnostics of phase changes in the course of bipolar disorder” (RPMA.01.02.00-14-5706/16-00). The authors thank psychiatrists that participated in the observational trial for their commitment and advice. The authors thank the researchers Weronika Radziszewska and Anna Olwert from Systems Research Institute, Polish Academy of Sciences for their support as well as Monika Dominiak and Professor Łukasz Świecicki for their support during the clinical assessment and for their advice about bipolar disorder.
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Hryniewicz, O., Kaczmarek-Majer, K., Opara, K.R. (2021). MAV Control Charts for Monitoring Two-State Processes Using Indirectly Observed Binary Data. In: Knoth, S., Schmid, W. (eds) Frontiers in Statistical Quality Control 13. ISQC 2019. Frontiers in Statistical Quality Control. Springer, Cham. https://doi.org/10.1007/978-3-030-67856-2_8
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