Engineering Commons^™

Human-Centered Approach For Resilience Assessment Of Healthcare Networks Subjected To Sequential Earthquakes, Emad Hassan, Hussam Mahmoud

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Healthcare services are critical for community resilience and stability as their role after disastrous events is indispensable to reducing casualties and returning the community to normalcy. Sequential mainshock-aftershock events can damage hospital components, reduce utility availability, and cause multiple patient surges. No studies have been conducted to evaluate the impact of mainshock-aftershock events on healthcare resilience. Accordingly, here we investigate the impact of multiple earthquakes on the healthcare network in Shelby County, Tennessee, and determine the optimal resources needed to enhance its functionality. The functionality is estimated by combining functionality quantity and quality terms. The quantity is calculated based on …

Advanced Machine Learning For Data-Driven Disease Prediction, Zekai Wang

Doctoral Dissertations

The rapid advancement in sensing and information technology has ushered us into an era of data explosion, where a large amount of data is now easily available and accessible in the clinical environment. This wealth of healthcare data offers new avenues for developing automated data-driven methods for disease diagnosis. Electronic Health Records (EHRs), serving as digital repositories of a patient's medical information, present unique opportunities to analyze and decipher clinical events and patterns within large populations. Given the rich information about a patient's health trajectory, leveraging EHRs through data-driven methodologies can significantly enhance clinical decision support systems.

However, utilizing real-world …

Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, And Machine Learning Techniques, Aysenur Betul Cengil

Graduate Theses and Dissertations

This dissertation investigates the utilization of telehealth services, initially focusing on the Arkansas healthcare system and then extending the analysis nationwide. It aims to understand the factors influencing telehealth adoption and its impact on healthcare delivery. After examining telehealth utilization in Arkansas from 2018 to 2022, the research utilizes a comprehensive dataset from Epic Cosmos, which includes a wide range of patient and visit data from multiple healthcare facilities across the United States from 2018 to 2023. This timeframe allows for a detailed analysis of telehealth trends before, during, and after the COVID-19 pandemic. In Chapter 2, we analyze key …

Sustainable Sensing With Paper Microfluidics: Applications In Health, Environment, And Food Safety, Sanjay Kumar, Jyoti B. Kaushal, Heow Pueh Lee

Durham School of Architectural Engineering and Construction: Faculty Publications

This manuscript offers a concise overview of paper microfluidics, emphasizing its sustainable sensing applications in healthcare, environmental monitoring, and food safety. Researchers have developed innovative sensing platforms for detecting pathogens, pollutants, and contaminants by leveraging the paper’s unique properties, such as biodegradability and affordability. These portable, low-cost sensors facilitate rapid diagnostics and on-site analysis, making them invaluable tools for resource-limited settings. This review discusses the fabrication techniques, principles, and applications of paper microfluidics, showcasing its potential to address pressing challenges and enhance human health and environmental sustainability.

Depression And Anxiety: Investigating The Impacts Of Scheduling On The Mental Health Of Healthcare Workers During The Covid-19 Pandemic, Christofer Gonzaga

All Theses

Depression is a growing problem in the United States and has ballooned into a globally recognized mental health issue that affects shift workers in various fields (Lee et al., 2017). Rates of depression and anxiety have been shown to be substantially increasing and the COVID-19 pandemic amplified this prevalence of depression and anxiety. In the United States, the prevalence of depression symptoms was more than 3- fold higher during COVID-19 compared to before the pandemic (Ettman et al., 2020). The delivery of healthcare services is an ongoing and dynamic process, requiring clinicians to work in various shifts to ensure the …

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Development Of Assistive Humanoid Robot, Soumayadeep Pal, Avrajeet Ghosh, Rupsa Bhattacharya, Atreyee Saha, Ankit Kar, Susmita Das

American Journal of Electronics & Communication (AJEC)

A dream of humanoid robot researchers is to develop a complete “human-like” (whatever that means) artificial agent both in terms of body and brain. It represents a significant breakthrough in the field of robotics, aiming to mimic and replicate human-like characteristics and functionalities. On the other hand, brain research has begun to produce computational models such as LIDA. In this paper, we propose an intermediate approach for body-brain integration in a form of a scenario-based distributed system. Busy hospital Emergency Departments (ED) are concerned with shortening the waiting times of patients, with relieving overburdened triage team physicians, nurses and medics, …

Design And Application Of Circular Edge-Fed Linearly Polarized Patch Antenna With Enhanced Bandwidth And Gain For Respiratory Monitoring Systems, Mo’Ath Akram Al Hayek

Theses

This Master’s thesis explores the design and application of a circular edge-fed patch antenna for respiratory monitoring systems. The antenna, fabricated from a thin copper adhesive sheet, demonstrates compatibility with textile materials of low thickness and a relative permittivity of 1.3. Using CST Studio Suite 3D EM simulation software, the antenna’s design was optimized through extensive trials, achieving improved bandwidth and gain. Our findings indicate that the antenna successfully differentiates between various breathing patterns, including slow, normal, and fast breathing rates. The study’s managerial and research implications extend to healthcare, wearable technology sectors, and the broader scientific community. The research …

Lean Six Sigma Body Of Knowledge For Healthcare Industry Administrators: Implementation Of Lessons Learned In Applied Engineering, Mohammed Ali

Technology Faculty Publications and Presentations

The purpose of this paper is to propose a Lean Six Sigma (LSS) course curriculum for healthcare administration and management majors. It identifies the relevant opportunities and challenges for the application of LSS within the healthcare industry. The paper also discusses the cultural changes necessary to provide an appropriate climate for its long-term success. This work contains a comprehensive description of the body of knowledge in LSS, which were successful in applied engineering. Additionally, the paper describes how LSS may be applied in the hospital setting to improve processes in patient-care services. Upon successful completion of the course, the healthcare …

Better Models For High-Stakes Tasks, Jacob Ryan Epifano

Theses and Dissertations

The intersection of machine learning and healthcare has the potential to transform medical diagnosis, treatment, and research. Machine learning models can analyze vast amounts of medical data and identify patterns that may be too complex for human analysis. However, one of the major challenges in this field is building trust between users and the model. Due to things like high false alarm rate and the black box nature of machine learning models, patients and medical professionals need to understand how the model arrives at its recommendations. In this work, we present several methods that aim to improve machine learning models …

A Study On The Field Of Xr Simulation Creation, Leveraging Game Engines To Develop A Vr Hospital Framework, Virat K. Tripathi

Electronic Thesis and Dissertation Repository

This thesis introduces an adaptable and extensible VR framework designed for clinicians and patients using pre-existing game development software like Blender and Unreal Engine. The framework aids patients in familiarizing themselves with hospital scenarios and environments, reducing anxiety, and improving navigation. Clinicians can use the tool to educate patients and collaboratively design new aspects of the environment. A prototype implementation demonstrates the system's effectiveness, with usability studies indicating that teleport movement is preferred over sliding for locomotion and that navigation speed can improve with subsequent trials in the VR simulator. The framework's potential for enhancing patient experience and facilitating informed …

Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer

School of Cybersecurity Faculty Publications

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …

Problem-Based Learning Of Heuristic Methods For Decision Problems In Mathematics, Computer Science And Industrial Engineering, Felix Engelhardt, Christina Büsing, Sabrina Schmitz

Practice Papers

In a digitalized world, most processes can be formalised, measured and described mathematically. The use of analytical methods to optimise such models and decisions constitutes operational research (OR), developing new methods for a specific problem and analysing them are part of discrete optimisation (DO). However, there is limited research on OR and application driven DO in higher education. Furthermore, neither is well integrated into engineering education research.

In this work, we present a case study of an interdisciplinary Master’s course on heuristic methods in the context of OR and DO. We discuss to what extent wellestablished approaches from engineering education …

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

Browse all Theses and Dissertations

This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …

The Use Of Data Balancing Algorithms To Correct For The Under-Representation Of Female Patients In A Cardiovascular Dataset, Sian Miller

Dissertations

Given that women are under-represented in medical datasets, and that machine learning classification algorithms are known to exhibit bias towards the majority class, the growing application of machine learning in the medical field risks resulting in worse medical outcomes for female patients. The Heart Failure Prediction (HFP) dataset is a historical dataset used for the training of models for the prediction of heart disease. This dataset contains significantly fewer female patients than male patients, and as such it is expected that models trained using this data will inherit a gender bias to favour male patients. This dissertation explores the use …

Quantum Computing And Its Applications In Healthcare, Vu Giang

OUR Journal: ODU Undergraduate Research Journal

This paper serves as a review of the state of quantum computing and its application in healthcare. The various avenues for how quantum computing can be applied to healthcare is discussed here along with the conversation about the limitations of the technology. With more and more efforts put into the development of these computers, its future is promising with the endeavors of furthering healthcare and various other industries.

Acm Web Conference 2023, Usha Lokala, Kaushik Roy, Utkarshani Jaimini, Amit Sheth

Publications

Improving the performance and explanations of ML algorithms is a priority for adoption by humans in the real world. In critical domains such as healthcare, such technology has significant potential to reduce the burden on humans and considerably reduce manual assessments by providing quality assistance at scale. In today’s data-driven world, artificial intelligence (AI) systems are still experiencing issues with bias, explainability, and human-like reasoning and interpretability. Causal AI is the technique that can reason and make human-like choices making it possible to go beyond narrow Machine learning-based techniques and can be integrated into human decision-making. It also offers intrinsic …

Essays On Perioperative Services Problems In Healthcare, Amogh S. Bhosekar

All Dissertations

One of the critical challenges in healthcare operations management is to efficiently utilize the expensive resources needed while maintaining the quality of care provided. Simulation and optimization methods can be effectively used to provide better healthcare services. This can be achieved by developing models to minimize patient waiting times, minimize healthcare supply chain and logistics costs, and maximize access. In this proposal, we study some of the important problems in healthcare operations management. More specifically, we focus on perioperative services and study scheduling of operating rooms (ORs) and management of necessary resources such as staff, equipment, and surgical instruments. We …

Deep Learning And Feature Engineering For Human Activity Recognition: Exploiting Novel Rich Learning Representations And Sub-Transfer Learning To Boost Practical Performance, Ria Kanjilal

USF Tampa Graduate Theses and Dissertations

A significant gap exists in our knowledge of how domain-specific feature extraction compares to unsupervised feature learning in the latent space of a deep neural network for a range of temporal applications including human activity recognition. This dissertation aims to address this gap specifically for human activity recognition using acceleration data. To ensure reproducibility, we use two publicly available datasets, UniMiB-SHAR and ExtraSensory, with a well-established history in the human activity recognition literature. We methodically analyze the performance of 64 different combinations of i) learning representations (in the form of raw temporal data or extracted features), ii) traditional and modern …

Ailbot: A Respiratory-Focused Symptom Checker Chatbot For Children, Gabrielle Mae V. Arco, Ken Ivan T. Cheng, Pamela S. Chong, Chico Andre G. Olaguer

DLSU Senior High School Research Congress

Inadequate access to healthcare, accompanied by the spread of COVID-19 has significantly affected most Filipinos. The prolonged quarantine period in the Philippines due to this virus restricts the movement and lifestyle of people. Thus, it makes medical appointments difficult to arrange. The ever-growing number of healthcare chatbots can provide access to healthcare check-ups quickly. These systems are still in their infancy stages, with the majority focusing on the mental and emotional well-being of people and catering to the general public. This study aims to develop a chatbot geared toward the respiratory health of Filipino children. Through the use of Chatfuel, …

Supplier Performance Scorecard Utilization In The Medical Device Manufacturing Healthcare Supply Chain, Justin Cardisco

Theses and Dissertations

The medical device manufacturing industry has a deficiency in determining how to improve supplier performance for the components and systems they purchase. Many complex medical devices require components from superb suppliers. But how does a medical device manufacturer (MDM) impartially assess supplier performance to know which suppliers to continuing with (or even boost purchase volumes) and which suppliers they should exit? This study describes which supplier-specific metrics are most important to medical device manufacturers (MDMs) so they can utilize this supplier performance scorecard backed by real-world inputs. This research will focus on five categories to measure MDM supplier performance (Quality, …

Machine Learning Used In Biomedical Computing And Intelligence Healthcare, Volume Ii, Honghao Gao, Ying Li, Zijian Zhang, Wenbing Zhao

Electrical and Computer Engineering Faculty Publications

No abstract provided.

Human-Machine Collaboration In Healthcare Innovation, Breeze Fenton

Electronic Theses and Dissertations

Almost every individual has visited a healthcare institute, whether for an annual checkup, surgery, or a nursing home. Ensuring healthcare institutes are using human-machine collaboration systems correctly can improve daily operations. A maturity assessment and an implementation plan have been developed to help healthcare institutes monitor the human-machine collaboration systems. A maturity model, the Smart Maturity Model for Health Care (SMMHC), is a tool designed for maturity assessment. A four-step implementation plan was also created in this research. The implementation plan views the maturity of the institute and develops a strategy on how to improve it. The research utilized Integrated …

Examining Cognitive Empathy Elements Within Ai Chatbots For Healthcare Systems, Lamia Alam

Dissertations, Master's Theses and Master's Reports

Empathy is an essential part of communication in healthcare. It is a multidimensional concept and the two key dimensions: emotional and cognitive empathy allow clinicians to understand a patient’s situation, reasoning, and feelings clearly (Mercer and Reynolds, 2002). As artificial intelligence (AI) is increasingly being used in healthcare for many routine tasks, accurate diagnoses, and complex treatment plans, it is becoming more crucial to incorporate clinical empathy into patient-faced AI systems. Unless patients perceive that the AI is understanding their situation, the communication between patient and AI may not sustain efficiently. AI may not really exhibit any emotional empathy at …

A Privacy Preserving Online Learning Framework For Medical Diagnosis Applications, Trang Pham Ngoc Nguyen

Theses: Doctorates and Masters

Electronic Health records are an important part of a digital healthcare system. Due to their significance, electronic health records have become a major target for hackers, and hospitals/clinics prefer to keep the records at local sites protected by adequate security measures. This introduces challenges in sharing health records. Sharing health records however, is critical in building an accurate online diagnosis framework. Most local sites have small data sets, and machine learning models developed locally based on small data sets, do not have knowledge about other data sets and learning models used at other sites.

The work in this thesis utilizes …

Single-Rail Adiabatic Logic For Energy-Efficient And Cpa-Resistant Cryptographic Circuit In Low-Frequency Medical Devices, Amit Degada, Himanshu Thapliyal

Electrical and Computer Engineering Graduate Research

Designing energy-efficient and secure cryptographic circuits in low-frequency medical devices are challenging due to low-energy requirements. Also, the conventional CMOS logic-based cryptographic circuits solutions in medical devices can be vulnerable to side-channel attacks (e.g. correlation power analysis (CPA)). In this article, we explored single-rail Clocked CMOS Adiabatic Logic (CCAL) to design an energy-efficient and secure cryptographic circuit for low-frequency medical devices. The performance of the CCAL logic-based circuits was checked with a power clock generator (2N2P-PCG) integrated into the design for the frequency range of 50 kHz to 250 kHz. The CCAL logic gates show an average of approximately 48% …

Linking Social Media, Medical Literature, And Clinical Notes Using Deep Learning., Mohsen Asghari

Electronic Theses and Dissertations

Researchers analyze data, information, and knowledge through many sources, formats, and methods. The dominant data format includes text and images. In the healthcare industry, professionals generate a large quantity of unstructured data. The complexity of this data and the lack of computational power causes delays in analysis. However, with emerging deep learning algorithms and access to computational powers such as graphics processing unit (GPU) and tensor processing units (TPUs), processing text and images is becoming more accessible. Deep learning algorithms achieve remarkable results in natural language processing (NLP) and computer vision. In this study, we focus on NLP in the …

Sos Explorer Application With Fuzzy-Genetic Algorithms To Assess An Enterprise Architecture -- A Healthcare Case Study, Josh Goldschmid, Vinayaka Gude, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS …

Nursing Home Study- Culminating Project Final Report, Ed Hale

Fire Protection Engineering: Culminating Experience Project Reports

This project report is a component of the culminating project required by the California Polytechnic State University, Master of Science, Fire Protection Engineering program. The purpose is to demonstrate the student’s comprehensive knowledge, skill, and ability to evaluate fire and life safety engineering principles utilizing both prescriptive and performance- based design as applied to the built environment. The student project building is a two-story, 78-bed, federally funded, government owned, skilled nursing facility with 90% of the occupants being mobility impaired.

The project building was designed and constructed using prescriptive codes. Fire and life safety structural components, means of egress, fire …