VictorJuiz/thesis huggingface dataset:
Mining electronic medical records for cancer treatment decisions
Keywords: Management Science and Engineering.

With the growing amount of medical data available and the increasing reliance of the medical community on artificial intelligence (AI) tools, there is an emerging demand for techniques that support medical decisions based on patient outcomes. To be adopted, machine learning (ML) tools must be accurate, trustworthy, and interpretable. Clinicians and patients should be able to understand the reasoning due to the high-stakes and sensitive nature of most medical decisions. Ultimately, medical ML tools will inform the decision-making process, empower clinicians and patients, and help improve clinical outcomes. We focus on using electronic medical records (EMRs) to improve retrospective comparative effectiveness research for reliable and interpretable decision-making in oncology. Decision making is fundamentally causal, intervening to improve outcomes. EMRs are a rich source of information that can be used to inform those decisions. Natural language processing (NLP) can analyze the unstructured clinical notes. Our research adapts causal inference, ML, and NLP techniques to discover insights from high-dimensional and high-noise EMR data. We study how we can 1) use NLP to identify cancer treatments, 2) reduce selection bias in observational studies, and 3) build ML tools to supplement radiologist decision making. In the first study, we use EMR notes to identify cancer treatments. We apply the method to prostate, esophagus, and oropharynx cancer datasets. It achieves over 90% accuracy for treatment identification. The method can be used to supplement cancer treatment records and help with future research on treatment planning and comparison. In the second study, we show how clinical notes can be used to uncover potential confounders and adjust for bias in retrospective comparative effectiveness studies. We apply our approach to prostate and lung cancer cohorts and found that we reduce the amount of bias when compared against established randomized control trials. In the third study, we develop a tool that could aid radiologist decision-making for mammogram diagnosis. It quantifies the decision threshold of each radiologist and can improve radiologist consistency and practice
Misallocation of Capital across Firm Size in Developing Countries
Keywords:  Department of Economics
Department of Economics
misallocation
developing countries
marginal returns to capital
small and medium sized enterprises
Thesis

In the development literature, there is a common conception that small firms disproportionately lack access to credit compared to large firms, leading them to under-invest in high return opportunities. Using firm-level data from the World Bank Enterprise Surveys in 106 developing countries, I investigate whether the distribution of returns to capital across firm size is consistent with this conception. Instead of finding higher returns to capital for smaller firms as predicted by the hypothesis, I find that the marginal return to capital increases with firm size in 43 countries. I then explore possible explanations behind this relationship involving heterogeneity in production elasticities, adjustment costs, markups, and risk. I find that a trade-off exists between the higher returns for large firms and higher levels of risk. After determining the relationship between returns to capital and firm size, I estimate that reallocating capital to eliminate misallocation related to firm size would lead to a 5% productivity gain on average.
Misconstruction of Older Adults: The Use of Sociotechnical Imaginaries in the Design of Older Adult Monitoring Technologies and the Conflicting Ethical Perspectives of Stakeholders
Keywords: monitoring technologies
eldercare
ethics of care
surveillance
sociotechnical imaginaries STS
Thesis

The older adult population in the U.S. is expected to more than double over the next 40 years and as this segment of the population expands, society will encounter an eldercare crisis. One promising solution is the use of monitoring technologies to ease the burden of care, however as described by STS scholar, Shelia Jasanoff, these technologies are constructed with certain assumptions in the design and implementation process that can be referred to as ‘sociotechnical imaginaries’. Through this thesis I highlight the social impetus, promise, and importance of the development of these technological solutions for senior care. I then explore technologists’ construction of the older adult lived experience – the ‘sociotechnical imaginaries’ - as made known through two real life case studies showcasing misuses of an older adult monitoring tool. My assessment concluded that these sociotechnical imaginaries are biased in their construction of older adult life and do not account for older adults' preferences. These limitations can make eldercare monitoring technologies unable to sufficiently care for older adults, can heighten older adult’s vulnerability, and can negatively impact older adult’s abilities to thrive. Finally, this misalignment of values in the design and implementation of older adult monitoring technologies highlights the conflicting ethical perspectives of eldercare stakeholders.
Misuse and manipulation : the strategic politics of military capacity building
Keywords: Political Science.

How do states exercise control over military partners, particularly when building their military capacity? Put another way, how do states prevent their partners from misusing transferred military power? Typical solutions to these delegation or principal-agent problems focus on the selection of partners with shared interests or the use conditional inducements that reward compliance and punish misuse. While these strategies are employed by states, they are often insufficient. This dissertation develops the argument that states also turn to a divierse set of strategies with a common theoretical logic that I call "strategies of manipulation." These strategies take a variety of forms including tailored capacity building, resource restrictions, counterbalancing and conflict mitigation. While inducements alter the partner's payoffs to alternative outcomes, manipulation alters the partner's efficiency in producing these alternative outcomes. Specifically, strategies of manipulation create incentives for the partner to use transferred resources to advance the delegating state's objective by mitigating the partner's opportunities to bring about other outcomes through misuse. Using a difference-in-differences analysis of panel data on post-World War II interstate arms transfers and U.S. military training, policymaker interviews and illustrative historical cases, I show that concerns about misuse and strategies of manipulation motivate decision makers at important moments in history and can help explain broad patterns of military capacity building
Mixed-method approaches to employment relationships in team-based online gig work
Keywords: Management Science and Engineering.

Fueled by internet-based technologies that enable seamless transactions between globally dispersed individuals, new online platforms are emerging that compose "flash" teams of remote freelancers to collaborate on complex projects in diverse industries. In these provisional teams, relationships are transient, virtual, and often algorithmically mediated. This presents organizational scholars with new intriguing puzzles. First, how do flash teams achieve effective coordination despite their lack of shared context and minimal opportunities for rich interactions? Second, how do workers navigate the uncertainties associated with this way of working, including those arising from being evaluated by algorithmic systems? To answer these questions, I draw on extensive online communication data (2 million Slack messages), the performance records of 117 teams, and over 18 months of field observation from a single "gig" labor platform company that composes virtual project teams of freelance software development professionals. Study 1 develops a natural language processing-based measure of teams' cognitive diversity as expressed in their online interactions and demonstrates its task-contingent relationship with performance. In a complementary inductive approach, study 2 theorizes how leaders' situational behaviors aimed at creating role clarity explain variation in team performance outcomes. Study 3 inductively theorizes the conditions under which freelancers developed heightened levels of trust and engagement in response to an algorithmic ranking system. My findings contribute to research on team effectiveness, team temporal dynamics, and leadership, as well as to the nascent literature on algorithms at work. They also point to several empirical and methodological opportunities for organizational scholars seeking to study novel technology-enabled organizational forms
Model interpretation and data valuation for machine learning
Keywords: Electrical Engineering.

Machine learning is being applied in various critical applications like healthcare. In order to be able to trust a machine learning model and to repair it once it malfunctions, it is important to be able to interpret its decision-making. For example, if a model's performance is poor on a specific subgroup (gender, race, etc), it is important to find out why and fix it. In this thesis, we examine the drawbacks of existing interpretability methods and introduce new ML interpretability algorithms that are designed to tackle some of the shortcomings. Data is the labor that trains machine learning models. It is not possible to interpret an ML model's behavior without going back to the data that trained it in the first place. A fundamental challenge is how to quantify the contribution of each source of data to the model's performance. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual datum. In this thesis, we discuss principled frameworks for equitable \emph{valuation} of data; that is, given a learning algorithm and a performance metric that quantifies the performance of the resulting model, we try to find the contribution of individual datum. This thesis is divided in 3 sections, machine learning interpretability and fairness, data valuation, and machine learning for healthcare - all linked by the common goal of making the use of machine learning more responsible for the benefit of human beings
Model-free methods for multiple testing and predictive inference
Keywords: Statistics.

Recent advances in technology have allowed us to collect, store and process an enormous amount of data, bringing unprecedented challenges to interpretable data analysis: first, the structure of data is often complicated, while model assumptions are hard to justify in practice; second, the algorithms used to analyze the data can be extremely complex---think of the convolutional neural nets---making it difficult to develop validity guarantees for the results. Indeed, it has been noticed by researchers that many of the classical statistical methods fail when applied to the modern type of problems---we need a new set of tools to conduct statistical data analysis in the modern era. This dissertation contributes to the toolbox of statistical data analysis in the modern world by presenting several model-free methods for multiple testing and predictive inference. The methods proposed in this dissertation, building upon knockoffs and conformal inference, bypass the modelling of the data structure and the analysis of complex algorithms, and work as wrappers of other (potentially black-box) existing algorithms. Despite the flexibility of these methods, they are guaranteed to achieve statistical validity under the minimal set of assumptions. The validity and efficacy of these methods are evaluated in extensive numerical experiments. Applying these methods to real genetic and clinical data has led to new scientific insights
Modeling and statistical analyses in global health policy
Keywords: Health Policy.

There is a need for more robust research that can inform decisions which will improve the health of low- and middle-income populations, especially those living in poverty. Enteric water-borne infections, such as cholera and typhoid, and undernutrition persist as leading causes of global child deaths and disproportionally affect the world's poor. This dissertation consists of three chapters aimed at informing policy that can reduce the burden of enteric infections and undernutrition. In the first chapter, I estimated the impact and cost-effectiveness of treatment and vaccination strategies to address cholera in Bangladesh and explored methods to more robustly and efficiently calibrate disease models to available data. I identified techniques to adapt a Bayesian calibration technique, Incremental Mixture Importance Sampling, to address large prior uncertainties in model parameters and biases in cholera reporting data caused by passive hospital-based surveillance systems. I found that routine cholera vaccination of young children is likely to be cost-effective in Bangladesh, but that uncertainties in cholera natural history identified in calibration translate to substantial uncertainties in the optimal vaccination policies that would remain unidentified without proper model calibration. My second chapter focused on using econometric techniques to evaluate the impact that a large US Government global nutrition and agriculture program, Feed the Future, has had on child nutrition outcomes in Sub-Saharan Africa. Feed the Future's focus countries experienced substantially larger declines in child stunting prevalence after the program's introduction, compared to non-focus countries. For my third chapter, I constructed a geographic- and age-stratified dynamic model of typhoid fever in India using primary data from an ongoing surveillance study and projected the incidence reductions and cost-effectiveness of several typhoid vaccination strategies. By incorporating new data on vaccine efficacy, costs of typhoid illness, and typhoid burdens within India, I found that typhoid vaccination is likely to result in net cost savings across most states in India, due to high costs of illness under the status quo and the concentration of burden among children, who are the most likely target population for vaccination efforts. My dissertation research highlights several methodological themes, including the importance of considering population heterogeneities in both statistical analysis and simulation modeling, and yields policy-relevant findings that can aid in more evidence-based decision-making
Modeling cardiovascular risks and diseases using human induced pluripotent stem cells and tissue engineering
Keywords: Bioengineering.

Cell-based, in vitro models of the cardiovascular system are invaluable for enhancing our understanding of cardiovascular health and disease. In recent years, the development of induced pluripotent stem cell (iPSC) technology has enabled effective modeling of cardiovascular diseases. In the first part of my thesis work, we utilized iPSC-derived cardiomyocytes (iPSC-CMs) to investigate the effects of microgravity on cardiomyocyte function and gene expression. This study represented both the first implementation of long-term human cell culture in space and the first use of iPSC technology to model the effects of spaceflight on human cardiomyocyte structure and function. The major drawback of using iPSC-CMs for cardiovascular modeling is their immature, fetal-like phenotype. In the second part of my thesis work, we utilized EHTs to enhance iPSC-CM maturation and further validate the results from two iPSC-CM studies that investigated (1) the mechanism by which a mutation in MYBPC3, a contractile protein, causes hypertrophic cardiomyopathy and (2) patient-specific responses to calcium channel blockers, a class of drugs commonly used to treat hypertension. In the final part of my thesis work, we utilized a sheep carotid interposition graft model to investigate whether a biodegradable external sheath could reduce graft maladaptation and failure. Collectively, this work describes the use of animal models, iPSCs, and tissue engineering to effectively model cardiovascular risks, disease, and drug responses
Modeling human driving from demonstrations
Keywords: Aeronautics and Astronautics.

For autonomous agents to coexist and cooperate with humans, it is important for them to anticipate human behavior. Models of human behavior can be used by autonomous agents to plan in response to human actions and proactively coordinate with humans. Such models can also be used to build simulators for testing autonomous agents by replicating the environment of operation. Modeling human behavior is challenging because of multiple reasons such as stochasticity, multi-modality, unobservable intents, high dimensional state action spaces, and nonlinear dynamics. In the literature, both ontological and phenomenological approaches have been used to model human behavior. While ontological approaches use rules, phenomenological approaches are data-driven. This thesis presents techniques to model human behavior from demonstrations. The techniques proposed in this thesis are evaluated on their ability to model human driving behavior which is important in autonomous driving for both planning and safety validation. First, this thesis adapts the technique of Generative Adversarial Imitation Learning to the problem of driver modeling. It extends the GAIL formulation to work in the multi-agent setting where observations gathered from multiple agents are used to inform the training process of a learning agent. The proposed method is shown to better imitate demonstrated driving as opposed to single agent learning method. Since driving has associated rules, the second part of this thesis introduces a method to provide domain knowledge to the imitation learning agent through reward augmentation. The proposed method, which relies on reward augmentation, is shown to provide better emergent driving performance and overall traffic flow in the recreated traffic simulations. Many of the applications of autonomous agents are safety-critical including that of autonomous driving. This makes it important for models to be interpretable. This thesis proposes a hybrid rule-based and data-driven method that relies on the technique of particle filtering to learn parameters of underlying rule-based models from human driving demonstrations. The proposed method is demonstrated on the problem of highway merging and shown to generate realistic driving behavior as assessed by a driving Turing test
Modeling multiple infectious diseases for cost-effectiveness analysis
Keywords: Management Science and Engineering.

Cost-effectiveness analyses can quantify and compare the benefits, harms, and costs of potential health interventions. Often, researchers will model a single disease for a cost-effectiveness analysis. However, some interventions can prevent multiple infectious diseases. For example, the Aedes aegypti and Aedes albopictus mosquitos transmit chikungunya, Zika, dengue, and yellow fever, and thus controlling these mosquitos can prevent cases of all four diseases. This dissertation focuses on applications of and methods for modeling multiple infectious diseases in cost-effectiveness analyses. First, I investigate if the results of a cost-effectiveness analysis can depend on the set of diseases that are modeled if some interventions prevent more than one disease. Next, I model both chikungunya and dengue to conduct a cost-effectiveness analysis of prevention measures for both diseases in Colombia. Finally, I develop conditions under which it is necessary to model multiple diseases when conducting a cost-effectiveness analysis and propose methods for using parallel modeling to simplify multi-disease modeling
Modeling multivariate time-series variables in healthcare using deep learning methods
Keywords: Electrical Engineering.

Well-being of patients or operation of healthcare systems can be evaluated by monitoring certain health metrics or workflow parameters. This monitoring is done by periodically measuring those metrics and evaluating their variations and changes over time. The resulting measurements create a set of data points along the temporal dimension, which are called time series data. Decisions to improve patient outcome or healthcare operation often depend on accurately forecasting future trends in those time series variables. Thus, time series analysis and prediction is of very high importance in healthcare provisioning. Variables in healthcare are often interrelated, and future values of a given variable depend on past observation and trends of that variable as well as other related variables. Thus, in many cases, time series prediction tasks in healthcare are multivariate. In this dissertation, we develop a multivariate time series predictive model that can simultaneously learn from multiple time series variables over a long temporal window. We implement this model as a convolutional neural network, which we call PatientFlowNet. We use this model to predict patient flow in hospital emergency departments. Specifically, we predict the rates at which patients arrive, are treated, and are discharged from the hospital. We benchmark our model against the state-of-the-art methods in patient flow prediction using data from emergency departments in three different hospitals. We then reuse our model for predicting vital signs for patients under anesthesia, namely heart rate as well as systolic and diastolic blood pressure. We observe that our model has superior prediction accuracy in the case of patient flow in hospital emergency departments where short-term and long-term trends are present but hidden in the data and variables are significantly interrelated. However, where long-term trends are not present, as is the case with patient vital signs under anesthesia, the performance of our model is similar to the best baseline method. The convolutional design of PatientFlowNet allows us to extract dependencies between input and output variables by examining the values of convolutional filters in the first layer of the network. We provide visual and interpretable representations of learned dependencies between time series variables in each study
Modeling the subglacial environment with geostatistics
Keywords: Geophysics.

The conditions beneath glaciers and ice-sheets including the topography, hydrology, and geology are essential for understanding ice dynamics, interpreting ice-sheet history, and predicting ice-sheet retreat and sea-level-rise in response to a changing climate. Located in remote regions and overlain by several kilometers of ice, subglacial conditions are difficult to characterize at ice-sheet scales. In this thesis, I explore the use geostatistical simulation and machine learning methods to characterize subglacial hydrologic and geologic conditions and produce the first ensemble realizations of stochastically simulated subglacial topography. In Chapter 2 of this thesis, I consider the physical and geological controls on the sizes and locations of Antarctic subglacial lakes. In Chapter 3, I investigate uncertainty in subglacial water routing at Jakobshavn Glacier in Greenland. In Chapter 4, I provide subglacial geological estimates for the Amundsen Sea Embayment in West Antarctica