Medicine

Amid the rapid advancements in clinical aesthetics, there lies an intricate gap between the artistic and sensory experience of facial aesthetics and the technical approach of medical sciences. As the field of clinical aesthetics veers further into the realm of the ideal, tensions arise between patient expectations and the practitioner's delivery. Central to this issue is the growing reliance on technology, which often overlooks the immediate sensory experience crucial for aesthetic satisfaction. Drawing inspiration from the arts and humanities, this dissertation introduces "Phenoesthetics" as an epistemological bridge, harmonizing the seemingly disparate domains of sensory experience and scientific analysis. By using visual art, particularly the composite works of the Renaissance artist Giuseppe Arcimboldo, as an illustrative tool, this work seeks to elucidate the dual nature of facial aesthetics perception: the universal, tangible forms and the more abstract, cultural interpretations. By weaving together elements from the arts, humanities, and sciences, this study propounds a Phenoesthetics approach — a comprehensive method designed to enhance understanding and communication in clinical aesthetics. The aim is to provide practitioners with a robust framework, fostering more aligned expectations and improving satisfaction rates in the field of aesthetic medicine.

The presence of artificial intelligence (AI) has incrementally increased in our lives since its introduction in the 1950s and has exponentially increased in the last decade. In medicine, AI holds the promise of providing complete panoramic views of a patient’s medical history, improving medical decision making, avoiding errors such as misdiagnosis and unnecessary procedures, interpretating tests and making treatment recommendations. In this study, I examine the influence of AI on decision-making behaviors and the changes to the professional institution of medicine. This paper links theories of institutional change and professions to further our understanding of the processes of change in response to emergent technology. Recognizing that the autonomy of decision making is central to the model of professional work, this study (1) shows how changes in decision-making processes are a driver of change in the institution of professions and (2) highlights how this impacts the professional role identity of health care providers which has implications for how medicine is taught and how diagnoses are made.

Though several clinical monitoring ways exist and have been applied to detect cardiac atril fibrillation (A-Fib) and other arrhythmia, these medical interventions and the ensuing clinical treatments are after the fact and costly. Current portable healthcare monitoring systems come in the form of Ambulatory Event Monitors. They are small, battery-operated electrocardiograph devices used to record the heart's rhythm and activity. However, they are not energy-aware ; they are not personalized ; they require long battery life, and ultimately fall short on delivering real-time continuous detection of arrhythmia and specifically progressive development of cardiac A-Fib. The focus of this dissertation is the design of a class of adaptive and efficient energy-aware real-time detection models for monitoring, early real-time detection and reporting of progressive development of cardiac A-Fib.... The design promises to have a greater positive public health impact from predicting A-Fib and providing a viable approach to meeting the energy needs of current and future real-time monitoring, detecting and reporting required in wearable computing healthcare applications that are constrained by scarce energy resources.

The implantable cardioverter defibrillator (ICD) is an electronic medical device that was invented by Dr. Michael Mirowski and his team in 1980. The purpose of the ICD, which is implanted in a person's chest, is to sense and shock the heart when detecting a lethal cardiac arrhythmia into a rhythm that can sustain life. While the ICD saves lives, it also has the potential to deliver painful shocks when it is activated. The ICD was initially inserted in people who had survived a sudden cardiac arrest; the device is now being implanted in older adults with heart failure and no known history of cardiac arrhythmias. When talking with patients and personal family members who had an ICD, it was unclear what influenced their decision to have an ICD implanted. Understanding the experience of decision-making for older adults who had an ICD has added to nursing knowledge, practice, and education when working with people who had an ICD inserted. To understand the lived experience, the researcher conducted a phenomenological research study, guided by the theoretical lens of Paterson and Zderad's (1976/1988) humanistic nursing and analyzed the data as outlined by Giorgi (2009). The results of the study indicated the participants' lived experience of decision-making for older adults who had an implantable cardioverter defibrillator inserted was influenced by the following : trust in their physician's decision; accepting the device was necessary; the decision was easy to make; and hope and desire to live longer.

The efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds on p based on stochastical considerations 3. Using the model developed on available (clinical) data to illustrate the bounds-limited performance of the prediction. Relevant analytical methods, computational efforts and simulated results are presented. Using the results compiled, the risk evaluation in medical diagnostics is discussed with real-world examples. Conclusions are enumerated and inferences are made with directions for future studies.

Epidemic models help us predict the outcome of an epidemic. I will discuss and compare two simple epidemic models: a deterministic model implemented by a simple differential equation, and a stochastic model, which is more realistic, but harder to analyze. In both models we assume, for simplicity, that each individual goes through only two stages: healthy (susceptible) and sick (infective). Such models, called SI epidemic models, describe infections with no immunity. We will show that, when the population gets large, the more realistic stochastic model approaches the simple deterministic model on the average, which will allow us to see that the deterministic model is used for a good reason.