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The purpose of this study was to create a screening tool specifically for the identification of sex trafficking victims in the medical setting through the analysis of existing human trafficking screening tool studies geared towards use in the medical setting. Screening questions from these studies were compiled and modified into

The purpose of this study was to create a screening tool specifically for the identification of sex trafficking victims in the medical setting through the analysis of existing human trafficking screening tool studies geared towards use in the medical setting. Screening questions from these studies were compiled and modified into a survey that was distributed to healthcare professionals through the nationwide HEAL (Health Professional Education, Advocacy, Linkage) Trafficking listserv. Each screening tool study demonstrated benefits and disadvantages that were helpful in the sampling and selection of screening tool questions. The small sample size and a lack of data on the attitudes of medical professionals on sex trafficked victims were noted as limitations to this study. Further implications for this study would include validating the screening tool questions in a medical setting to determine the sensitivity of the survey in identifying patients as possible sex trafficking victims.
ContributorsCatano, Karen Samantha (Co-author) / Byun, Jiwon (Co-author) / Roe-Sepowitz, Dominique (Thesis director) / Lee, Maurice (Committee member) / School for the Science of Health Care Delivery (Contributor) / College of Integrative Sciences and Arts (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Many developing countries do not have health care systems that can afford technological biomedical devices or supplies to make such devices operational. To fill this void, nonprofit organizations, like Project C.U.R.E., recondition retired biomedical instrumentation so they can send medical supplies to help these developing countries. One of the issues

Many developing countries do not have health care systems that can afford technological biomedical devices or supplies to make such devices operational. To fill this void, nonprofit organizations, like Project C.U.R.E., recondition retired biomedical instrumentation so they can send medical supplies to help these developing countries. One of the issues with this is that sometimes the devices are unusable because components or expendable supplies are not available (Bhadelia). This issue has also been shown in the Impact Evaluations that Project C.U.R.E. receives from the clinics that explain the reasons why certain devices are no longer in use. That need underlies the idea on which this honors thesis has come into being. The purpose of this honors project was to create packing lists for biomedical instruments that Project C.U.R.E. recycles. This packing list would decrease the likelihood of important items being forgotten when sending devices. If an extra fuse, battery, light bulb, cuff or transducer is the difference between a functional or a nonfunctional medical device, such a list would be of benefit to Project C.U.R.E and these developing countries. In order to make this packing list, manuals for each device were used to determine what supplies were required, what was necessary for cleaning, and what supplies were desirable but functionally optional. This list was then added into a database that could be easily navigated and could help when packing up boxes for a shipment. The database also makes adding and editing the packing list simple and easy so that as Project C.U.R.E. gets more donated devices the packing list can grow.
ContributorsGraft, Kelsey Anne (Author) / Coursen, Jerry (Thesis director) / Walters, Danielle (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic

Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.

Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.

As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment.

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form

In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training and then for an entire network. Using the technique of supervised learning, the neural network is trained with three main factors for housing price classification, including its total number of rooms, bathrooms, and square footage. Once trained with most of the generated data set, it is tested for accuracy by introducing the remainder of the data-set and observing how closely its computed output for each set of inputs compares to the target value. From a programming perspective, the artificial neuron is implemented in C so that it would be more closely tied to the operating system and therefore make the collected profiler data more precise during the program's execution. The program is designed to break down each stage of the neuron's training process into distinct functions. In addition to utilizing more functional code, the struct data type is used as the underlying data structure for this project to not only represent the neuron but for implementing the neuron's training and test data. Once fully trained, the neuron's test results are then graphed to visually depict how well the neuron learned from its sample training set. Finally, the profiler data is analyzed to describe how the program operated from a data management perspective on the software and hardware level.
ContributorsRichards, Nicholas Giovanni (Author) / Miller, Phillip (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Description
Physician-assisted suicide occurs when a physician facilitates a patient's death by prescribing a lethal medication that they understand will be used for the purpose of ending the patient's life. It is a highly contentious subject and, with the recent addition of California to the list of states that allow physician-assisted

Physician-assisted suicide occurs when a physician facilitates a patient's death by prescribing a lethal medication that they understand will be used for the purpose of ending the patient's life. It is a highly contentious subject and, with the recent addition of California to the list of states that allow physician-assisted suicide, is an increasingly relevant subject. Physician-assisted suicide is rarely framed as a healthcare experience, despite being a choice in the process of end-of-life care. The research seeks to bring together the debates about physician-assisted suicide with conversations about health care experiences. The experiences and perspectives of young people are particularly valuable to evaluate now, as their voices will soon be the leaders in the debate over physician-assisted suicide. Within this research, there is an underlying theme of independence of individuals that is present through both the literature review and the body of data collected and analyzed. The study found that there was no significant relationship between the quality of a person's healthcare and their perspectives about physician-assisted suicide.
ContributorsMoeur, Katherine Elizabeth (Author) / Brian, Jennifer (Thesis director) / Graff, Sarah (Committee member) / Stevenson, Christine (Committee member) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
This study investigates how the patient-provider relationship between lesbian, gay, and bisexual women and their healthcare providers influences their access to, utilization of, and experiences within healthcare environments. Nineteen participants, ages 18 to 34, were recruited using convenience and snowball sampling. Interviews were conducted inquiring about their health history and

This study investigates how the patient-provider relationship between lesbian, gay, and bisexual women and their healthcare providers influences their access to, utilization of, and experiences within healthcare environments. Nineteen participants, ages 18 to 34, were recruited using convenience and snowball sampling. Interviews were conducted inquiring about their health history and their experiences within the healthcare system in the context of their sexual orientation. The data collected from these interviews was used to create an analysis of the healthcare experiences of those who identify as queer. Although the original intention of the project was to chronicle the experiences of LGB women specifically, there were four non-binary gender respondents who contributed interviews. In an effort to not privilege any orientation over another, the respondents were collectively referred to as queer, given the inclusive and an encompassing nature of the term. The general conclusion of this study is that respondents most often experienced heterosexism rather than outright homophobia when accessing healthcare. If heterosexism was present within the healthcare setting, it made respondents feel uncomfortable with their providers and less likely to inform them of their sexuality even if it was medically relevant to their health outcomes. Gender, race, and,socioeconomic differences also had an effect on the patient-provider relationship. Non-binary respondents acknowledged the need for inclusion of more gender options outside of male or female on the reporting forms often seen in medical offices. By doing so, medical professionals are acknowledging their awareness and knowledge of people outside of the binary gender system, thus improving the experience of these patients. While race and socioeconomic status were less relevant to the context of this study, it was found that these factors have an affect on the patient-provider relationship. There are many suggestions for providers to improve the experiences of queer patients within the healthcare setting. This includes nonverbal indications of acknowledgement and acceptance, such as signs in the office that indicate it to be a queer friendly space. This will help in eliminating the fear and miscommunication that can often happen when a queer patient sees a practitioner for the first time. In addition, better education on medically relevant topics to queer patients, is necessary in order to eliminate disparities in health outcomes. This is particularly evident in trans health, where specialized education is necessary in order to decrease poor health outcomes in trans patients. Future directions of this study necessitate a closer look on how race and socioeconomic status have an effect on a queer patient's relationship with their provider.
Created2016-05
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Description
In the medical industry, there have been promising advances in the increase of new types of healthcare to the public. As of 2015, there was a 98% Premarket Approval rate, a 38% increase since 2010. In addition, there were 41 new novel drugs approved for clinical usage in 2014 where

In the medical industry, there have been promising advances in the increase of new types of healthcare to the public. As of 2015, there was a 98% Premarket Approval rate, a 38% increase since 2010. In addition, there were 41 new novel drugs approved for clinical usage in 2014 where the average in the previous years from 2005-2013 was 25. However, the research process towards creating and delivering new healthcare to the public remains remarkably inefficient. It takes on average 15 years, over $900 million by one estimate, for a less than 12% success rate of discovering a novel drug for clinical usage. Medical devices do not fare much better. Between 2005-2009, there were over 700 recalls per year. In addition, it takes at minimum 3.25 years for a 510(k) exempt premarket approval. Plus, a time lag exists where it takes 17 years for only 14% of medical discoveries to be implemented clinically. Coupled with these inefficiencies, government funding for medical research has been decreasing since 2002 (2.5% of Gross Domestic Product) and is predicted to be 1.5% of Gross Domestic Product by 2019. Translational research, the conversion of bench-side discoveries to clinical usage for a simplistic definition, has been on the rise since the 1990s. This may be driving the increased premarket approvals and new novel drug approvals. At the very least, it is worth considering as translational research is directly related towards healthcare practices. In this paper, I propose to improve the outcomes of translational research in order to better deliver advancing healthcare to the public. I suggest Best Value Performance Information Procurement System (BV PIPS) should be adapted in the selection process of translational research projects to fund. BV PIPS has been shown to increase the efficiency and success rate of delivering projects and services. There has been over 17 years of research with $6.3 billion of projects and services delivered showing that BV PIPS has a 98% customer satisfaction, 90% minimized management effort, and utilizes 50% less manpower and effort. Using University of Michigan \u2014 Coulter Foundation Program's funding process as a baseline and standard in the current selection of translational research projects to fund, I offer changes to this process based on BV PIPS that may ameliorate it. As concepts implemented in this process are congruent with literature on successful translational research, it may suggest that this new model for selecting translational research projects to fund will reduce costs, increase efficiency, and increase success. This may then lead to more Premarket Approvals, more new novel drug approvals, quicker delivery time to the market, and lower recalls.
ContributorsDel Rosario, Joseph Paul (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
Healthcare systems and health insurance are both concepts implemented in every country to provide access to the general population. Countries undergo healthcare reforms in order to increase the performance of the system. In 2010, the Affordable Care Act (ACA) was introduced in the United States to increase coverage and create

Healthcare systems and health insurance are both concepts implemented in every country to provide access to the general population. Countries undergo healthcare reforms in order to increase the performance of the system. In 2010, the Affordable Care Act (ACA) was introduced in the United States to increase coverage and create a more inclusive health insurance market. For comparison, the recent reforms in Chile and Singapore were observed as points to determine what concepts work well and what can be implemented in the U.S. system. Unlike the United States, Chile and Singapore completely altered the system that was previously in use. In Chile, the reforms began in the 1970s and made two more major changes in 1973 and early 2000s. Singapore began its reform in the 1960s and created the medical savings account system that is still in use today. To analyze the system further, the medical professions of neurology, physician assistants and optometry were compared in each country. In regards to neurology, the coverage of services in Chile and Singapore are similar in that select medical procedures are covered. In contrast, the United States offers coverage on a case-by-case basis. For physician assistants, such a profession does not exist in Chile or Singapore. In the United States, the profession is rapidly expanding, and coverage is offered for most services provided. Optometry is a stand-alone profession in both the U.S. and Singapore. The services provided by the optometrists are selectively covered by insurance, depending on whether it is considered a medical problem. Chile covers the services often provided by optometrists, however, the ophthalmologist is the provider, as optometry does not exist. This study concluded that the U.S. should continue to provide a more inclusive healthcare system that includes vision and dental care. The U.S., like Singapore, should also adopt a more integrative system. Under this system, patient care would be provided in a way that professionals specializing in the care are included in every step of the process.
ContributorsLa, Jenny (Co-author) / Feruj, Farihah (Co-author) / Morrison, Sarah (Co-author) / Gaughan, Monica (Thesis director) / Essary, Alison (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05