Towards autonomous diagnostic systems with medical imaging

Democratizing access to high quality healthcare has highlighted the need for autonomous diagnostic systems that a non-expert can use. Remote communities, first responders and even deep space explorers will come to rely on medical imaging systems that will provide them with Point of Care diagnostic capabilities. This thesis introduces the building blocks that would enable the creation of such a system. Firstly, we present a case study in order to further motivate the need and requirements of autonomous diagnostic systems. This case study primarily concerns deep space exploration where astronauts cannot rely on communication with earth-bound doctors to help them through diagnosis, nor can they make the trip back to earth for treatment. Requirements and possible solutions about the major challenges faced with such an application are discussed. Moreover, this work describes how a system can explore its perceived environment by developing a Multi Agent Reinforcement Learning method that allows for implicit communication between the agents. Under this regime agents can share the knowledge that benefits them all in achieving their individual tasks. Furthermore, we explore how systems can understand the 3D properties of 2D depicted objects in a probabilistic way. In Part II, this work explores how to reason about the extracted information in a causally enabled manner. A critical view on the applications of causality in medical imaging, and its potential uses is provided. It is then narrowed down to estimating possible future outcomes and reasoning about counterfactual outcomes by embedding data on a pseudo-Riemannian manifold and constraining the latent space by using the relativistic concept of light cones. By formalizing an approach to estimating counterfactuals, a computationally lighter alternative to the abduction-action-prediction paradigm is presented through the introduction of Deep Twin Networks. Appropriate partial identifiability constraints for categorical variables are derived and the method is applied in a series of medical tasks involving structured data, images and videos. All methods are evaluated in a wide array of synthetic and real life tasks that showcase their abilities, often achieving state-of-the-art performance or matching the existing best performance while requiring a fraction of the computational cost.Open Acces

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets

Confounding-Robust Policy Improvement with Human-AI Teams

Human-AI collaboration has the potential to transform various domains by leveraging the complementary strengths of human experts and Artificial Intelligence (AI) systems. However, unobserved confounding can undermine the effectiveness of this collaboration, leading to biased and unreliable outcomes. In this paper, we propose a novel solution to address unobserved confounding in human-AI collaboration by employing the marginal sensitivity model (MSM). Our approach combines domain expertise with AI-driven statistical modeling to account for potential confounders that may otherwise remain hidden. We present a deferral collaboration framework for incorporating the MSM into policy learning from observational data, enabling the system to control for the influence of unobserved confounding factors. In addition, we propose a personalized deferral collaboration system to leverage the diverse expertise of different human decision-makers. By adjusting for potential biases, our proposed solution enhances the robustness and reliability of collaborative outcomes. The empirical and theoretical analyses demonstrate the efficacy of our approach in mitigating unobserved confounding and improving the overall performance of human-AI collaborations.Comment: 24 page

The detection of two-component mixture alternatives:Theory and methods

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some known distribution, e.g., Gaussian white noise, while in the alternative one assumes that among observations there might be a cluster of points carrying a signal, which is characterized by some distribution G. The main research objective is to determine a detectable set of alternatives in the parameter space combining the parameters of G and the mixture proportion p. We focus is on the finite sample sizes and wish to study the possibility of detecting alternatives for fixed n, given pre-specified error levels. The first part of the thesis covers theoretical results. Specifically, we introduce a parametrization which relates the parameter space of an alternative to the sample size, and present the regions of detectability and non-detectability. The regions of detectability are the subsets of the new parameter space (induced by the parametrization) where for a prespecified type I error rate, the type II error rate of the likelihood ratio test (LRT) is bounded from above by some constant. To move towards the real data applications, we also check the performance of some non-parametric testing procedures proposed for this problem and some widely used distributions. In the second part of the thesis, we use this argument to develop a framework for clinical trial designs aimed at detecting a sensitive-to-therapy subpopulation. The idea of modeling treatment response as a mixture of subpopulations originates from treatment effect heterogeneity. Methods studying the effects of heterogeneity in the clinical data are referred to as subgroup analyses. However, designs accounting for possible response heterogeneity are rarely discussed, though in some cases they might help to avoid trial failure due to the lack of efficacy. In our work, we consider two possible subgroups of patients, drug responders and drug non-responders. Given no preliminary information about patients' memberships, we propose a framework for designing randomized clinical trials that are able to detect a responders' subgroup of desired characteristics. We also propose strategies to minimize the number of enrolled patients whilst preserving the testing errors below given levels and suggest how the design along with all testing metrics can be generalized to the case of multiple centers. The last part of the thesis is not directly related to the preceding parts. We present two supervised classification algorithms for real-data applications

The Examination of Nonparametric Person-Fit Statistics as Appropriate Measures of Response Bias in Ordered Polytomous Items

Survey research is ubiquitous within the social sciences; however, surveys are vulnerable to response biases. Response biases introduce construct-irrelevant variance into survey responses, which degrades the accuracy of conclusions drawn through the use of surveys. Nonparametric person-fit statistics have been shown to accurately identify response biases in dichotomous response data but are not well studied in polytomous response data. This study examines the accuracy of nonparametric person-fit statistics in polytomous response data. A 6 x 4 x 4 x 2 simulation study was conducted, with type of aberrancy (6), number of response options (4), dimensionality (4), and test length (2) as factors. The sensitivity, specificity, positive predictive value, and negative predictive value for U3, the normed number of Guttman errors, and HTi were calculated using a bootstrapped cutoff. Findings indicate that these person-fit statistics with a conservative cutoff had excellent specificity but poor sensitivity. Advisor: Kurt F. Geisinge

"911, Is This an Emergency?": How 911 Call-Takers Extract, Interpret, and Classify Caller Information

Policing in America is in crisis. Much of the nation is outraged by the level and distribution of encounters and arrests, infringements on civil liberties, and excessive uses of force by the police. Prior scholarship typically has attributed these problems to features of officer-initiated policing—specifically police officers’ decisions in who to stop and when to arrest. By contrast, reactive or call-driven policing has not received comparable scholarly attention. Yet, in many places roughly half of all police-work involves responding to the public’s calls-for-service. In these cases, a series of interactions take place between 911 callers, 911 call-takers, and dispatchers before the police arrive at the scene, all of which can produce information that shapes police responses. This dissertation is squarely focused on the role of 911 in American policing. It aims to answer the question of how 911 call-takers mediate caller demands and impact policing in the field. To answer this central research question, the author worked for two years as a 911 call-taker in Southeast Michigan, which allowed her to analyze the kinds of problems callers report, the decisions that call-takers must make, the challenges and dilemmas that they face, and the ways in which training and organizational norms shape the call-taking process. Using a mix of quantitative, qualitative, and conversation analytic methods, this dissertation reveals that the process through which private citizens’ requests become police responses is complex and presents unique challenges to policing. The chapters aim to show how the contemporary 911 system has come to offer the public wide latitude over the scope of police work. By dissecting the day-to-day duties of 911 call-takers, the chapters shine a light on two critical call-taking functions. First, the author reveals an overlooked call-taker function—risk appraisal. Through unpacking precisely how call-takers appraise risk, namely through extraction, interpretation, and classification of caller information, this dissertation provides a framework to evaluate call-taker actions. Second, the author complicates the previously documented gatekeeping function by showing how organizational rules and norms can constrain the ability of 911 call-takers to limit the public’s heavy reliance on the system. Taken together, the chapters find that call-takers exercise discretion when performing these critical functions and their actions impact police responses. This dissertation puts forth recommendations aimed at encouraging police agencies to reconceptualize the call-taking function in an effort to enable call-takers to more intelligently deploy discretion. Recommendations include developing protocols and criteria that empower call-takers to prevent inappropriate requests from receiving police services, training call-takers to assess risk in more sophisticated ways, distributing call-taker best practices to peers, and using technology to assist call-takers in preserving caller uncertainty. The author hopes that these findings and recommendations will help improve police encounters with the public and spur readers to strongly consider 911’s role in policing in the future.PHDPublic Policy & SociologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163046/1/jgillool_1.pd