Comparison of control charts for monitoring clinical performance using binary data.

BACKGROUND: Time series charts are increasingly used by clinical teams to monitor their performance, but statistical control charts are not widely used, partly due to uncertainty about which chart to use. Although there is a large literature on methods, there are few systematic comparisons of charts for detecting changes in rates of binary clinical performance data. METHODS: We compared four control charts for binary data: the Shewhart p-chart; the exponentially weighted moving average (EWMA) chart; the cumulative sum (CUSUM) chart; and the g-chart. Charts were set up to have the same long-term false signal rate. Chart performance was then judged according to the expected number of patients treated until a change in rate was detected. RESULTS: For large absolute increases in rates (>10%), the Shewhart p-chart and EWMA both had good performance, although not quite as good as the CUSUM. For small absolute increases (<10%), the CUSUM detected changes more rapidly. The g-chart is designed to efficiently detect decreases in low event rates, but it again had less good performance than the CUSUM. IMPLICATIONS: The Shewhart p-chart is the simplest chart to implement and interpret, and performs well for detecting large changes, which may be useful for monitoring processes of care. The g-chart is a useful complement for determining the success of initiatives to reduce low-event rates (eg, adverse events). The CUSUM may be particularly useful for faster detection of problems with patient safety leading to increases in adverse event rates.  

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart

Hybrid Statistical and Deep Learning Models for Diagnosis and Prognosis in Manufacturing Systems

In today’s highly competitive business environment, every company seeks to work at their full potential to keep up with competitors and stay in the market. Manager and engineers, therefore, constantly try to develop technologies to improve their product quality. Advancements in online sensing technologies and communication networks have reshaped the competitive landscape of manufacturing systems, leading to exponential growth of Condition Monitoring (CM) data. High-dimensional data sources can be particularly important in process monitoring and their efficient utilization can help systems reach high accuracy in fault diagnosis and prognosis. While researches in Statistical Process Control (SPC) tools and Condition-Based Maintenance (CBM) are tremendous, their applications considering high-dimensional data sets has received less attention due to the complexity and challenging nature of such data and its analysis. This thesis adds to this field by designing a Deep Learning (DL) based survival analysis model towards CBM in the prognostic context and a DL and SPC based hybrid model for process diagnosis, both using high dimensional data. In the first part, we a design support system for maintenance decision making by considering degradation signals obtained from CM data. The decision support system in place can predict system’s failure probability in a smart way. In the second part, a Fast Region-based Convolutional Network (Fast R-CNN) model is applied to monitor the video input data. Then, specific statistical features are derived from the resulting bounding boxes and plotted on the multivariate Exponentially Weighted Moving Average (EWMA) control chart to monitor the process

On the Monitoring of Simple Linear Berkson Profiles

[[abstract]]We consider the quality of a process, which can be characterized by a simple linear Berkson profile. One existing approach for monitoring the simple linear profile and two new proposed schemes are studied for charting the simple linear Berkson profile. Simulation studies demonstrate the effectiveness and efficiency of one of the proposed monitoring schemes. In addition, a systematic diagnostic approach is provided to spot the change point location of the process and to identify the parameter of change in the profile. Finally, an example from semiconductor manufacturing is used to illustrate the implementation of the proposed monitoring scheme and diagnostic approach.[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙

A DATA ANALYTICAL FRAMEWORK FOR IMPROVING REAL-TIME, DECISION SUPPORT SYSTEMS IN HEALTHCARE

In this dissertation we develop a framework that combines data mining, statistics and operations research methods for improving real-time decision support systems in healthcare. Our approach consists of three main concepts: data gathering and preprocessing, modeling, and deployment. We introduce the notion of offline and semi-offline modeling to differentiate between models that are based on known baseline behavior and those based on a baseline with missing information. We apply and illustrate the framework in the context of two important healthcare contexts: biosurveillance and kidney allocation. In the biosurveillance context, we address the problem of early detection of disease outbreaks. We discuss integer programming-based univariate monitoring and statistical and operations research-based multivariate monitoring approaches. We assess method performance on authentic biosurveillance data. In the kidney allocation context, we present a two-phase model that combines an integer programming-based learning phase and a data-analytical based real-time phase. We examine and evaluate our method on the current Organ Procurement and Transplantation Network (OPTN) waiting list. In both contexts, we show that our framework produces significant improvements over existing methods

Application of quality control charts for early detection of flood hazards

Flooding is a serious and devastating hazard that many countries face regularly. Timely detection of these changes can help us manage rainwater and hence flooding. Numerous statistical tools are available for the early detection of these changes. In this regard, application of control charts is an effective choice toward monitoring natural events. In this study, we used self-proposed and existing control charts as a means of early detection of changes in the rainfall data of Pakistan as a case study. The proposed methodology covered two aspects: one to enhance the ability of Shewhart control charts toward detection of small or moderate changes in the behavior of natural events, and another to offer skewness correction based on individual control chart under runs rules for managing these natural events – especially when collected data follows unknown skewed distribution. Results elucidate that control charts structures were efficient in early detection and prediction of floods during 2010. Our results are in accordance with the theoretical results of existing studies, and we propose similar methods to be used for meteorological purposes

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way

Machine Learning Modeling for Image Segmentation in Manufacturing and Agriculture Applications

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringShing I ChangThis dissertation focuses on applying machine learning (ML) modelling for image segmentation tasks of various applications such as additive manufacturing monitoring, agricultural soil cover classification, and laser scribing quality control. The proposed ML framework uses various ML models such as gradient boosting classifier and deep convolutional neural network to improve and automate image segmentation tasks. In recent years, supervised ML methods have been widely adopted for imaging processing applications in various industries. The presence of cameras installed in production processes has generated a vast amount of image data that can potentially be used for process monitoring. Specifically, deep supervised machine learning models have been successfully implemented to build automatic tools for filtering and classifying useful information for process monitoring. However, successful implementations of deep supervised learning algorithms depend on several factors such as distribution and size of training data, selected ML models, and consistency in the target domain distribution that may change based on different environmental conditions over time. The proposed framework takes advantage of general-purposed, trained supervised learning models and applies them for process monitoring applications related to manufacturing and agriculture. In Chapter 2, a layer-wise framework is proposed to monitor the quality of 3D printing parts based on top-view images. The proposed statistical process monitoring method starts with self-start control charts that require only two successful initial prints. Unsupervised machine learning methods can be used for problems in which high accuracy is not required, but statistical process monitoring usually demands high classification accuracies to avoid Type I and II errors. Answering the challenges of image processing using unsupervised methods due to lighting, a supervised Gradient Boosting Classifier (GBC) with 93 percent accuracy is adopted to classify each printed layer from the printing bed. Despite the power of GBC or other decision-tree-based ML models to comparable to unsupervised ML models, their capability is limited in terms of accuracy and running time for complex classification problems such as soil cover classification. In Chapter 3, a deep convolutional neural network (DCNN) for semantic segmentation is trained to quantify and monitor soil coverage in agricultural fields. The trained model is capable of accurately quantifying green canopy cover, counting plants, and classifying stubble. Due to the wide variety of scenarios in a real agricultural field, 3942 high-resolution images were collected and labeled for training and test data set. The difficulty and hardship of collecting, cleaning, and labeling the mentioned dataset was the motivation to find a better approach to alleviate data-wrangling burden for any ML model training. One of the most influential factors is the need for a high volume of labeled data from an exact problem domain in terms of feature space and distributions of data of all classes. Image data preparation for deep learning model training is expensive in terms of the time for labelling due to tedious manual processing. Multiple human labelers can work simultaneously but inconsistent labeling will generate a training data set that often compromises model performance. In addition, training a ML model for a complication problem from scratch will also demand vast computational power. One of the potential approaches for alleviating data wrangling challenges is transfer learning (TL). In Chapter 4, a TL approach was adopted for monitoring three laser scribing characteristics – scribe width, straightness, and debris to answer these challenges. The proposed transfer deep convolutional neural network (TDCNN) model can reduce timely and costly processing of data preparation. The proposed framework leverages a deep learning model already trained for a similar problem and only uses 21 images generated gleaned from the problem domain. The proposed TDCNN overcame the data challenge by leveraging the DCNN model called VGG16 already trained for basic geometric features using more than two million pictures. Appropriate image processing techniques were provided to measure scribe width and line straightness as well as total scribe and debris area using classified images with 96 percent accuracy. In addition to the fact that the TDCNN is functioning with less trainable parameters (i.e., 5 million versus 15 million for VGG16), increasing training size to 154 did not provide significant improvement in accuracy that shows the TDCNN does not need high volume of data to be successful. Finally, chapter 5 summarizes the proposed work and lays out the topics for future research