Matching Items (103)
![152165-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-08/152165-Thumbnail%20Image.png?versionId=i15TuqnM5VkYuXA287QXXeDGkbjbg3TO&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240601/us-west-2/s3/aws4_request&X-Amz-Date=20240601T055849Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=67c918cbbb3ec58384fc2ef3a340d90d1a0c2f5eb6a40ee78a1c1e41abef7bf0&itok=MIcep070)
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
![151499-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/151499-Thumbnail%20Image.png?versionId=pWRPS4crzClSTwjFwCJF25OQRZA30fQZ&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T095610Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=b03752f3171e2e3e22a449f1623800c4b871c3166bbdbbdc2e008d7c0443bdaf&itok=kr2Z2Yrq)
Description
Parkinson's disease, the most prevalent movement disorder of the central nervous system, is a chronic condition that affects more than 1000,000 U.S. residents and about 3% of the population over the age of 65. The characteristic symptoms include tremors, bradykinesia, rigidity and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced complications, such as dyskinesia and motor fluctuation. With the disease progress, clinicians must closely monitor patients' progress in order to identify any complications or decline in motor function as soon as possible in PD management. Unfortunately, current clinical assessment for Parkinson's is subjective and mostly influenced by brief observations during patient visits. Thus improvement or decline in patients' motor function in between visits is extremely difficult to assess. This may hamper clinicians while making informed decisions about the course of therapy for Parkinson's patients and could negatively impact clinical care. In this study we explored new approaches for PD assessment that aim to provide home-based PD assessment and monitoring. By extending the disease assessment to home, the healthcare burden on patients and their family can be reduced, and the disease progress can be more closely monitored by physicians. To achieve these aims, two novel approaches have been designed, developed and validated. The first approach is a questionnaire based self-evaluation metric, which estimate the PD severity through using self-evaluation score on pre-designed questions. Based on the results of the first approach, a smart phone based approach was invented. The approach takes advantage of the mobile computing technology and clinical decision support approach to evaluate the motor performance of patient daily activity and provide the longitudinal disease assessment and monitoring. Both approaches have been validated on recruited PD patients at the movement disorder program of Barrow Neurological Clinic (BNC) at St Joseph's Hospital and Medical Center. The results of validation tests showed favorable accuracy on detecting and assessing critical symptoms of PD, and shed light on promising future of implementing mobile platform based PD evaluation and monitoring tools to facilitate PD management.
ContributorsPan, Di (Author) / Petitti, Diana (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Dhall, Rohit (Committee member) / Arizona State University (Publisher)
Created2013
![152123-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-08/152123-Thumbnail%20Image.png?versionId=jMipKiYd4dPa.8xRup_4_6sAG31nRkZh&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240530/us-west-2/s3/aws4_request&X-Amz-Date=20240530T153824Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=effbadd8af959cd44bec92b372e118f8e8f4068810355af558f787698ae12a57&itok=tS72118i)
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
![152417-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/152417-Thumbnail%20Image.png?versionId=9DvvwPcTOgN1JiWYsYtLZ4cxZ3AD4qj4&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T134030Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=b0a3aa8d05c8108759bb9378f87185711442470ef108e0c73e54c9b77d2ed641&itok=QIr1C0KN)
Description
Medical students acquire and enhance their clinical skills using various available techniques and resources. As the health care profession has move towards team-based practice, students and trainees need to practice team-based procedures that involve timely management of clinical tasks and adequate communication with other members of the team. Such team-based procedures include surgical and clinical procedures, some of which are protocol-driven. Cost and time required for individual team-based training sessions, along with other factors, contribute to making the training complex and challenging. A great deal of research has been done on medically-focused collaborative virtual reality (VR)-based training for protocol-driven procedures as a cost-effective as well as time-efficient solution. Most VR-based simulators focus on training of individual personnel. The ones which focus on providing team training provide an interactive simulation for only a few scenarios in a collaborative virtual environment (CVE). These simulators are suited for didactic training for cognitive skills development. The training sessions in the simulators require the physical presence of mentors. The problem with this kind of system is that the mentor must be present at the training location (either physically or virtually) to evaluate the performance of the team (or an individual). Another issue is that there is no efficient methodology that exists to provide feedback to the trainees during the training session itself (formative feedback). Furthermore, they lack the ability to provide training in acquisition or improvement of psychomotor skills for the tasks that require force or touch feedback such as cardiopulmonary resuscitation (CPR). To find a potential solution to overcome some of these concerns, a novel training system was designed and developed that utilizes the integration of sensors into a CVE for time-critical medical procedures. The system allows the participants to simultaneously access the CVE and receive training from geographically diverse locations. The system is also able to provide real-time feedback and is also able to store important data during each training/testing session. Finally, this study also presents a generalizable collaborative team-training system that can be used across various team-based procedures in medical as well as non-medical domains.
ContributorsKhanal, Prabal (Author) / Greenes, Robert (Thesis advisor) / Patel, Vimla (Thesis advisor) / Smith, Marshall (Committee member) / Gupta, Ashish (Committee member) / Kaufman, David (Committee member) / Arizona State University (Publisher)
Created2014
![150897-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/150897-Thumbnail%20Image.png?versionId=pejAg.iAQGGAB7HeyTsCzyXSPEDN8EDC&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240605/us-west-2/s3/aws4_request&X-Amz-Date=20240605T232204Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=dd62a4017f513c5b889cef888dbe1fa917fd149d81572034918809266495e46f&itok=-zjM8Dvj)
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
![151020-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-08/151020-Thumbnail%20Image.png?versionId=12mTV7J3plsZ8Lin1FNhEDuJ1Y5ssO3Q&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T084725Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=28d3203d5791fe18261c3f79648334d590752e5bc9a684147310ea6c6fd48e38&itok=WRePeP5o)
Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
![150708-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/150708-Thumbnail%20Image.png?versionId=wXPoc_cAKyhxHOwIFbyXPfxa46NsKdfI&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240605/us-west-2/s3/aws4_request&X-Amz-Date=20240605T232204Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=7d47811fb2961f530e753a8110d6e0f51d4cb83b7c097789cd9a0be2da48cc60&itok=fvJHaiTi)
Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
![151234-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/151234-Thumbnail%20Image.png?versionId=uxKFr5XfzgM8dA8I7xsv581tqFxSpqXs&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240606/us-west-2/s3/aws4_request&X-Amz-Date=20240606T015825Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=64973332f6c5ab51ec6665cd6529440a26b0f96ad7bec67c0b705946a2ad0913&itok=Md4w2CIN)
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
I travelled and worked in international fisheries policy for 7 months in preparation for this thesis. During this time I completed one internship in Rome, Italy with the Food and Agriculture Organization of the United Nations (UNFAO) and another internship on the island of Pohnpei with the Secretariat of the Western and Central Pacific Fisheries Commission (WCPFC). From these experiences, I selected the subject of this thesis. My thesis analyzes the management system for South Pacific albacore tuna, the source stock for brands like "Chicken of the Sea" and "Starkist". South Pacific albacore tuna pass through international waters and the waters of several Pacific Island countries and territories, necessitating States to cooperate and coordinate to sustain the future viability of the stock. A case study for transboundary natural resource management, I discuss the institutional complexity that arises from managing such a resource. I use common-pool resource (CPR) theory to describe this complexity, which frames natural resource management as a collective-action problem among resource users. I first conceptualize the management system as having multiple institutional scales and multiple levels of organization. Then, employing Ostrom's 8 design principles for successful CPR management, I conduct a multi-institution analysis of the international, regional, and subregional institutions that participate in the management system. Finally, I also conduct a cross-institution analysis by examining the interactions between these institutions. I find that significant space for theoretical development exists in CPR theory for understanding complex management systems for transboundary natural resources. Furthermore, I find that interactions between institutions create linkages that could be retooled to improve the performance of the South Pacific albacore tuna management system.
ContributorsAbolhassani, Angela Maryam (Author) / Abbott, Kenneth (Thesis director) / Schoon, Michael (Committee member) / Barrett, The Honors College (Contributor) / School of Politics and Global Studies (Contributor) / School of Life Sciences (Contributor) / Department of English (Contributor)
Created2015-05
![136273-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-05/136273-Thumbnail%20Image.png?versionId=Kyre7GIYbaM9Yg1ahzA1uhDYsAtL6BeA&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240530/us-west-2/s3/aws4_request&X-Amz-Date=20240530T153633Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=2451b0bd483ca69d1ebe677d0e324183e113815991d62534495b7cd7a66522f1&itok=5y3WGKEe)
Description
This paper explores two areas of study: Colony Collapse Disorder and urban apiculture--the practice of keeping bees in urban areas. Additionally, this paper discusses the ways in which Colony Collapse Disorder has encouraged an increase in urban beekeeping, and the possible role of urban apiculture as a means of combatting the negative effects of Colony Collapse Disorder. The symptoms, history, and possible causes of Colony Collapse Disorder are presented, as well as the important role that honey bees play in human agriculture. Following the discussion of Colony Collapse Disorder is a description of my urban beekeeping apprenticeship at Desert Marigold School where I kept bees, researched various hives, attended a beekeeping workshop in Tucson, and eventually built a hive and established a colony with my mentor. This paper includes a guide to beekeeping basics, as well as a guide to starting a hive based upon the lessons learned during my apprenticeship.
ContributorsRomero, Madelyn Rattan (Author) / Schoon, Michael (Thesis director) / Silcox, Holly (Committee member) / Barrett, The Honors College (Contributor) / School of Sustainability (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2015-05