Machine Learning Control Charts for Monitoring Serially Correlated Data

Xie, Xiulin; Qiu, Peihua

doi:10.1007/978-3-030-83819-5_6

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Non-stationary processes: It is important to assess the stationarity of the process to model it correctly; otherwise, the abnormal patterns of the non-stationary component of the process could be missed, and the rate of false alarms in the resulting chart could be larger than expected [67], [70]. Healthcare examples are in disease outbreak detection studies [14], [19], where the disease has a seasonal component and where the IC distributions of the biomarkers in longitudinal studies of patients change through time [44], [71].…”

Section: B Challenges and Limitations Of Using Data-driven Methods Co...mentioning

confidence: 99%

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A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.INDEX TERMS Data-driven, healthcare, scientometric, statistical process monitoring.

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Section: B Challenges and Limitations Of Using Data-driven Methods Co...mentioning

confidence: 99%

“…High-reliability processes: A limitation arises when datasets do not have enough OC data to train most DD methods offline [67], [70]. There are some proposed strategies, such as one-class classifiers [73], [74], or artificial contrasts [75], but more work is needed.…”

Section: B Challenges and Limitations Of Using Data-driven Methods Co...mentioning

confidence: 99%

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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Machine Learning Approaches for Statistical Process Control

Qiu

2024

Wiley StatsRef: Statistics Reference Online

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Machine learning approaches are widely used in different applications, including the ones involving statistical process control (SPC). In a traditional SPC problem (e.g., online monitoring of a production line), a small set of in‐control (IC) process observations is routinely collected before online process monitoring for estimating certain parameters of the IC process distribution. This dataset, however, does not contain any out‐of‐control (OC) process observations. Thus, supervised machine learning methods would be inappropriate to use in such cases since they require a training dataset that contains both IC and OC process observations. To overcome this difficulty, some machine learning methodologies specific for SPC have been developed based on one‐class classifications, artificial contrast, real‐time contrast, transparent sequential learning, and more. This article provides an overview on these methods.

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Dynamic Process Monitoring Using Machine Learning Control Charts

Xie

Qiu

2023

Springer Series in Reliability Engineering

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Machine Learning Control Charts for Monitoring Serially Correlated Data

Cited by 5 publications

References 25 publications

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

Machine Learning Approaches for Statistical Process Control

Dynamic Process Monitoring Using Machine Learning Control Charts

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