Fadel M. Megahed

Predicting heart transplantation outcomes through data analytics

Predicting the survival of heart transplant patients is an important, yet challenging problem since it plays a crucial role in understanding the matching procedure between a donor and a recipient. Data mining models can be used to effectively analyze and extract novel information from large/complex transplantation datasets. The objective of this study is to predict the 1-, 5-, and 9-year patients graft survival following a heart transplant surgery via the deployment of analytical models that are based on four powerful classification algorithms (i.e. decision trees, artificial neural networks, support vector machines, and logistic regression). Since the datasets used in this study has a much larger number of survival cases than deaths for 1- and 5-year survival analysis and vice versa for 9-year survival analysis, random under sampling (RUS) and synthetic minority over-sampling (SMOTE) are employed to overcome the data-imbalance problems. The results indicate that logistic regression combined with SMOTE achieves the best classification for the 1-, 5-, and 9-year outcome prediction, with area-under-the-curve (AUC) values of 0.624, 0.676, and 0.838, respectively. By applying sensitivity analysis to the data analytical models, the most important predictors and their associated contribution for the 1-, 5-, and 9-year graft survival of heart transplant patients are identified. By doing so, variables, whose importance changes over time, are differentiated. Not only this proposed hybrid approach gives superior results over the literature but also the models and identification of the variables present important retrospective findings, which can be the basis for a prospective medical study. A data-driven approach for predicting survival outcomes at multiple time-points is developed.The method successfully predicted short-, mid-, & long-term heart transplantation outcomes.The proposed method is unique in that it fills an important gap in the published literature.The approach is generic so it can be applied to other organ transplantation cases.

Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective

The increasing availability of high-volume, high-velocity data sets, often containing variables of different data types, brings an increasing need for monitoring tools that are designed to handle these big data sets. While the research on multivariate statistical process monitoring tools is vast, the application of these tools for big data sets has received less attention. In this expository paper, we give an overview of the current state of data-driven multivariate statistical process monitoring methodology. We highlight some of the main directions involving statistical learning and dimension reduction techniques applied to control charts in research from supply chain, engineering, computer science, and statistics. The goal of this paper is to bring into better focus some of the monitoring and surveillance methodology informed by data mining techniques that show promise for monitoring large and diverse data sets. We introduce an example using Wikipedia search information and illustrate a few of the complexities of applying the available methods to a high-dimensional monitoring scenario. Throughout, we offer advice to practitioners and some suggestions for future research in this emerging area of research.

An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products

Image-capturing systems are increasingly being used in manufacturing shop floors since they can reliably capture important aesthetic information pertaining to the quality of manufactured parts in real time. State-of-the-art image-monitoring applications have focused on the detection of a single fault; however, the number of fault clusters per image in industrial applications can be numerous. To address this issue, we propose the use of a multivariate generalized likelihood ratio (MGLR) control chart for monitoring industrial products whose quality is described by a specific pattern (e.g. uniform patterns in LED screens or decorative patterns in textile products). Our method is specifically designed for greyscale images that are typical outputs of real-time industrial image-capturing systems. Extensive computer simulations show that the proposed method can detect the occurrence of single and multiple faults. We also present an experimental study to highlight how practitioners can implement and make use of the MGLR control chart in image-monitoring applications.

Stock market one-day ahead movement prediction using disparate data sources

A financial expert system for predicting the daily stock movements.Knowledge base captures both traditional and online data sources.The inference engine uses three artificial intelligence techniques.Prediction accuracy of 85% is higher than the reported results in the literature.The system is hosted online and freely available for investors and researchers. There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of crowd-sourced knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the publics collective interest. Google and Wikipedia have become important knowledge bases for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our inference engine. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.

Statistical Perspectives on “Big Data”

As our information infrastructure evolves, our ability to store, extract, and analyze data is rapidly changing. Big data is a popular term that is used to describe the large, diverse, complex and/or longitudinal datasets generated from a variety of instruments, sensors and/or computer-based transactions. The term big data refers not only to the size or volume of data, but also to the variety of data and the velocity or speed of data accrual. As the volume, variety, and velocity of data increase, our existing analytical methodologies are stretched to new limits. These changes pose new opportunities for researchers in statistical methodology, including those interested in surveillance and statistical process control methods. Although it is well documented that harnessing big data to make better decisions can serve as a basis for innovative solutions in industry, healthcare, and science, these solutions can be found more easily with sound statistical methodologies. In this paper, we discuss several big data applications to highlight the opportunities and challenges for applied statisticians interested in surveillance and statistical process control. Our goal is to bring the research issues into better focus and encourage methodological developments for big data analysis in these areas.

A survey of the prevalence of fatigue, its precursors and individual coping mechanisms among U.S. manufacturing workers

Advanced manufacturing has resulted in significant changes on the shop-floor, influencing work demands and the working environment. The corresponding safety-related effects, including fatigue, have not been captured on an industry-wide scale. This paper presents results of a survey of U.S. manufacturing workers for the: prevalence of fatigue, its root causes and significant factors, and adopted individual fatigue coping methods. The responses from 451 manufacturing employees were analyzed using descriptive data analysis, bivariate analysis and Market Basket Analysis. 57.9% of respondents indicated that they were somewhat fatigued during the past week. They reported the ankles/feet, lower back and eyes were frequently affected body parts and a lack of sleep, work stress and shift schedule were top selected root causes for fatigue. In order to respond to fatigue when it is present, respondents reported coping by drinking caffeinated drinks, stretching/doing exercises and talking with coworkers. Frequent combinations of fatigue causes and individual coping methods were identified. These results may inform the design of fatigue monitoring and mitigation strategies and future research related to fatigue development.

A machine learning approach to detect changes in gait parameters following a fatiguing occupational task

Abstract The purpose of this study is to provide a method for classifying non-fatigued vs. fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction (

Streamlining science with structured data archives: insights from stroke rehabilitation

1 Recognizer) along with the support vector machine model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring. The classifier distinguished between states using distance-based scores from the recogniser and the step duration. The results of fatigue detection showed an accuracy of 90% across data from 20 recruited subjects. This method utilises the minimum amount of data and features from only one low-cost sensor to reliably classify the state of fatigue induced by a realistic manufacturing task using a simple machine learning algorithm that can be extended to real-time fatigue monitoring as a future technology to be employed in the manufacturing facilities. Practitioner Summary: We examined the use of a wearable sensor for the detection of fatigue-related changes in gait based on a simulated manual material handling task. Classification based on foot acceleration and position trajectories resulted in 90% accuracy. This method provides a practical framework for predicting realistic levels of fatigue.

Effects of Task Type, Task Duration, and Age on Body Kinematics and Subjective Fatigue

Recent advances in bibliometrics have focused on text-mining to organize scientific disciplines based on author networks, keywords, and citations. These approaches provide insights, but fail to capture important experimental data that exist in many scientific disciplines. The objective of our paper is to show how such data can be used to organize the literature within a discipline, and identify knowledge gaps. Our approach is especially important for disciplines relying on randomized control trials. Using stroke rehabilitation as an informative example, we construct an interactive graphing platform to address domain general scientific questions relating to bias, common data elements, and relationships between key constructs in a field. Our platform allows researchers to ask their own questions and systematically search the literature from the data up.

Optimization of Split Keyboard Design for Touchscreen Devices

Investigating the effects of workload on body kinematics is the first step to identify, monitor, and ultimately reduce the incidence of fatigue, a prevalent phenomenon in the workplace that leads to chronic disorders, loss of productivity, and absenteeism (Lu, Megahed, Sesek, & Cavuoto, 2017, In Press; Ricci, Chee, Lorandeau, & Berger, 2007). In fa- tigue monitoring, kinematic measures including acceleration, jerk, and body posture have been found to be informative (Lu et al., 2017, In Press; Maman, Yazdi, Cavuoto, & Megahed, 2017; Ricci et al., 2007); however, none of the previous studies have considered a comprehensive set of these kinematic metrics during simulated manufacturing tasks. This study assessed the effects of duration, ease of task, and age as three factors on different body kinematic metrics and subjective ratings as a substitute “ground truth” for fatigue development. This will serve to inform feature selection for modeling fatigue development over a broad range of industrial tasks. Nineteen participants (divided into two age groups of younger (<25, 6 males and 4 females) and older (>40, 8 males and 1 female)) completed three, three-hour sessions of parts assembly (light), supply pickup and insertion (moderate), and manual material handling (difficult). Inertial measurement units (IMUs) were attached on right wrist, middle of the trunk, the right side of the hip, and right ankle. The mean and peak values of acceleration, jerk, and posture for each body location along with the minimum value (with respect to the horizontal plane) of trunk bending posture were considered as the kinematic variables of interest. The Borg rating of perceived exertion (RPE) and subjective fatigue level (SFL) were recorded at the start of each session, and then every ten minutes for RPE, and every thirty minutes for SFL. Perceived workload, rated using the NASA Task Load Index (TLX), was obtained every one hour. The TLX, RPE, and SFL at the end of hour 1 (the first time point where all three ratings are obtained) were considered as the pre-fatigue values and at hour 3 of the tasks as the post-fatigue. Similarly, the pre-fatigue kinematic data of the IMUs was the period from minutes 10 to 20 and the post-fatigue data was the period from minutes 160 to 170. The results of repeated measures ANOVA showed a significant time (p < 0.001) effect on all three subjective ratings. In addition, time and age interacted to affect RPE (p = 0.018) with a 36% increase in younger and 28% in the older group. Time had significant effects only on a few of kinematic variables including mean trunk acceleration (8% decreased), mean trunk posture (3% less bent), and peak hip acceleration (10% increased) after fatigue. Moreover, there was a significant age and task interaction for peak hip acceleration (~1 m/s2 decreased), mean and peak leg posture (~4o increased and ~12o decreased, respectively), and minimum trunk posture (~5o increased) from the younger group to the older group. In addition, there was significant interaction (p = 0.011) between time and task in bending posture denoted by hip and trunk, which provides insight into the different effects of fatigue on different tasks, i.e., 2% more bending after fatigue in manual material handling and supply pickup and insertion in comparison to parts assembly. This increased bending angle following fatigue was in agreement with the findings of Strohrmann, Harms, Kappeler-Setz, and Troster (2012). There were significant differences between the younger group to the older group in terms of kinematics, i.e., peak hip acceleration, mean and peak leg posture, and minimum trunk posture that may be attributable to different quadriceps strength and postural stability between the age groups. Overall, the results present a set of kinematic parameters influenced by fatigue; however, further analysis is required to explore more temporal and spatial movement variables from IMUs for a better understanding of fatigue effects and indicators.