Optimizing Implanted Cardiac Device Follow-Up Care

Cardiovascular implantable electronic devices (CIEDs) are life-saving devices programmed to detect cardiac arrhythmias and intervene with pacing or shocks to avoid cardiac death. Currently, three to four million Americans rely on CIEDs and this number is growing rapidly with approximately 400,000 new device implantations each year. Worldwide, around one million new device implantations are performed annually. CIEDs consist of battery-powered pulse generators connected to the heart by one or more electrode wires, called "leads," embedded within a patient's vein. To achieve the maximum possible clinical benefit, modern CIEDs can automatically transmit data to the clinician's office through various media, such as email and text messaging, to allow for remote monitoring. This dissertation concentrates on improving the quality of care of patients with CIEDs, i.e., maximizing the expected lifetime of these patients, by focusing on three major challenges inherent to these devices: (i) cardiac leads fail stochastically and it is not clear whether to abandon them or to extract them, either immediately or at a later time; (ii) the average life span of CIED batteries is not as long as the average patient's expected lifetime and it is not clear when to replace the battery-powered pulse generators; (iii) the remote monitoring of CIEDs can adversely affect the battery's remaining lifetime and it is not clear how frequently the remote transmissions should be performed. We use methodologies including Markov decision processes as well as applied probability and statistics to formulate and analyze decision models that enable clinicians to provide patients with better quality of care. Using clinical data and expert opinion, we carefully calibrate the models concerning challenges (i) and (ii); for (iii), we provide insightful numerical examples for a stylized model. Our results suggest that behaving optimally can significantly extend patients' lives while simultaneously decreasing the burden on the healthcare system by reducing the number of surgeries, in-office visits, and so on, without compromising the patients' well-being

Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $\chi^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $\chi^2$-detector can achieve a high anomaly detection performance.Comment: Accepted to be Published in: 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Delft, Netherlands, 2020, pp. 156-161. arXiv admin note: text overlap with arXiv:1911.0153

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems

Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Human factors and ergonomics have played an essential role in increasing the safety and performance of operators in the nuclear energy industry. In this critical review, we examine how artificial intelligence (AI) technologies can be leveraged to mitigate human errors, thereby improving the safety and performance of operators in nuclear power plants (NPPs). First, we discuss the various causes of human errors in NPPs. Next, we examine the ways in which AI has been introduced to and incorporated into different types of operator support systems to mitigate these human errors. We specifically examine (1) operator support systems, including decision support systems, (2) sensor fault detection systems, (3) operation validation systems, (4) operator monitoring systems, (5) autonomous control systems, (6) predictive maintenance systems, (7) automated text analysis systems, and (8) safety assessment systems. Finally, we provide some of the shortcomings of the existing AI technologies and discuss the challenges still ahead for their further adoption and implementation to provide future research directions

Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling

The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease

INFORMS Editor\u27s Cut: Diversity & Inclusion: Analytics for Social Impact

Diversity has recently become the Swiss Army Knife of policy, management, and practice in the United States, and in many other countries as well. The concept seems to fit perfectly into the language of modern politics, referring to plurality, harmony, exchange, tolerance, and fairness. Its main characteristic is an extreme sort of plasticity, which allows its users to connect the term to a wide range of ideas. It refers to all manner of difference: personal identity, professional practice styles, community characteristics.This wide range of meanings cuts both ways: on one hand, it has legitimized the term; on the other hand, it may have emptied the term of true meaning or normative values. To understand how diversity can make a tangible difference in our society, we must address as well how organizations and societies can become, and be seen as, fair (‘equity’) and how our institutions can ensure that diversity and equity can persist and deepen over time and changes in social and economic circumstances (‘inclusion’).In operations research, management science, and analytics, diversity, equity, and inclusion is often understood as primarily a topic of organizational design and management; however, it can also be viewed as a lens through which inquiry across many other application areas and analytic methods can be enriched. Diversity, equity, and inclusion can be viewed as a problem that the profession must solve (for example, enlarging the pipeline); however, it can also be viewed as a worldview that can transform the discipline and perhaps society at large.This Editor\u27s Cut highlights a number of especially interesting points of view, seeking to highlight outstanding case studies but also internal contradictions, because diversity can refer to completely different things in universities, political parties, or the business world. In information technology, diversity can help understand how different kinds of collaboration styles and roles are associated with project success. In management, diversity can help understand how leadership, human capital, and social capital are associated with organization success. In social policy, diversity can help understand how underrepresented and marginalized groups can benefit from programs and initiatives that respond to historic inequities, and better contribute to individual and community well-being. But can’t diversity, equity, and inclusion also help workgroups, firms, government, and communities to do better, and be better, by confronting difference, resolving conflict, establishing, and enforcing social norms and legal obligations, providing opportunity, helping to ensure beneficial outcomes?Through academic research, professional practice, and community service, we hope that this collection of peer-reviewed journal articles, book chapters, magazine articles, webinars, videos, and podcasts can help all of us find our own way to put diversity, equity, and inclusion closer to the center of what we do in the decision sciences.The complete volume is available online at https://pubsonline.informs.org/editorscut/diversity