A survey of deep learning models in medical therapeutic areas.

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Explaining deep neural networks: A survey on the global interpretation methods

A substantial amount of research has been carried out in Explainable Artificial Intelligence (XAI) models, especially in those which explain the deep architectures of neural networks. A number of XAI approaches have been proposed to achieve trust in Artificial Intelligence (AI) models as well as provide explainability of specific decisions made within these models. Among these approaches, global interpretation methods have emerged as the prominent methods of explainability because they have the strength to explain every feature and the structure of the model. This survey attempts to provide a comprehensive review of global interpretation methods that completely explain the behaviour of the AI models. We present a taxonomy of the available global interpretations models and systematically highlight the critical features and algorithms that differentiate them from local as well as hybrid models of explainability. Through examples and case studies from the literature, we evaluate the strengths and weaknesses of the global interpretation models and assess challenges when these methods are put into practice. We conclude the paper by providing the future directions of research in how the existing challenges in global interpretation methods could be addressed and what values and opportunities could be realized by the resolution of these challenges

Operations Research & Statistical Learning Methods to Monitor the Progression of Glaucoma and Chronic Diseases

This thesis focuses on advancing operations research and statistical learning methods for medical decision making to improve the care of patients diagnosed with chronic conditions. Because the National Center for Disease Prevention (2020) estimates chronic conditions affect approximately 60% of the US adult population, improving the care of patients with chronic conditions will improve the lives of most Americans. Patients diagnosed with chronic conditions face lifestyle changes, rising treatment costs, and frequently reductions in quality of life. To improve the way in which clinicians treat patients with chronic conditions, treatment decisions can be supplemented by evidenced-based, data driven algorithmic decision-making methods. This thesis provides data-driven methodologies of a general nature that are instantiated for several medical decision-making problems. In chapter two we proactively identify the time of a patient’s primary open angle glaucoma (POAG) progression under high measurement error conditions using a soft voting ensemble classification model. When medical tests have low residual variability (e.g., empirical difference between the patient's true and recorded value is small) they can effectively, without the use of sophisticated methods, identify the patient's current disease phase; however, when medical tests have moderate to high residual variability this may not be the case. We present a solution to the latter case. We find rapid progression disease phases can be proactively identified with the combination of denoising and supervised classification methods. In chapter three, we determine the optimal time to next follow-up appointment for patients with the chronic condition of ocular hypertension (OHTN). Patients with OHTN are at increased risk of developing glaucoma and should be observed over their lifetime. Follow-up appointment schedules that are chosen poorly can result in, at minimum, delay in the detection of a patient’s progression to glaucoma, and at worse, yield poor patient outcomes. To this end, we present a personalized decision support algorithm that uses the fitted Q-iteration reinforcement learning algorithm to recommend personalized time-to-next follow-up schedules that are based on a patient’s medical state. We find personalized follow-up appointments schedules produced by reinforcement learning methods are superior to both 1-year and 2-year fixed interval follow-up appointment schedules. In chapters four and five, we examine and compare several criteria for determining progression from OHTN to POAG and evaluate the use of a collective POAG conversion rule in predicting future occurrences of patients' POAG conversion. We find age, race, and sex are statistically significant determinants in progression for all compared criteria. However, there exists broad conversion discordance between the criteria, as demonstrated by statistically different survival curves and the limited overlap in eyes that progressed by multiple criteria. Ultimately, to permit machine learning models to predict conversion from OHTN to POAG, it is essential to have quantitative reference standards for POAG conversion for researchers to use. Additionally, using the collective POAG conversion rule, we find machine learning models can successfully predict future OHTN conversion events to POAG. This research was conducted in collaboration with clinical disease/domain experts. All the medical decision-making research herein addresses real world healthcare issues, that, if solved, have the potential to improve vision care if implemented. While these methodologies primarily focus on chronic conditions affecting the eyes (e.g., OHTN and POAG), it is important to note that much of the work produced offers methods applicable to other chronic diseases.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169926/1/isaacaj_1.pd

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered