Browsing by Subject "Medical informatics"
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Item Estimation and personalization of clinical insulin therapy parameters(2013-08) Palma, Ramiro Cesar, IV; Edgar, Thomas F.; Heller, AdamDespite considerable effort considerable cost in both time and money, as many as two out of three persons with type 1 diabetes are not in control of their disease. As a result, 40% of these individuals will go on to develop at least one serious complication including retinopathy, nephropathy, neuropathy and cardiomyopathy. It is further estimated that as much as $4 billion could be saved annually if all persons with type 1 diabetes in the US were properly controlled. Adequate treatment of type 1 diabetes is predicated on the estimation of three clinical insulin therapy parameters: the basal dose, the insulin sensitivity factor and the insulin-to-carbohydrate ratio. Currently, these therapy parameters are determined by iterative titration procedures based on expert opinion. Unfortunately, there is evidence suggesting that for the majority of individuals, these titration protocols do not provide good results. In this work we develop an alternative to traditional insulin titration protocols that allows clinical insulin therapy parameters to be estimated directly from a set of easily acquired measurements. First, a simple model of type 1 diabetes is used to derive a series of equations connecting the model's parameters to the clinically important insulin therapy parameters of insulin sensitivity factor, insulin-to-carbohydrate ratio and basal insulin dose. The simplifying assumptions used to derive these equations are tested and shown to be valid and the Fisher Information Matrix is used to demonstrate parameter identifiability. Parameter estimation is then performed on two cohorts of virtual subjects, as well as two segments of real continuous glucose monitoring data from a person with type 1 diabetes. Identification of the true insulin therapy parameters is successful under most conditions for both cohorts of virtual subjects. Parameter estimation for one of the two segments of real continuous glucose monitoring data is also successful. Finally, because continuous glucose monitors are instrumental to successful implementation of our insulin therapy framework, the physiological environment in which continuous glucose monitoring takes place is modeled and a fundamental limitation on measurement precision is shown to exist. An examination of physiological variability in the parameters indicates that many of the challenges observed in real world continuous glucose monitoring may have a relationship to changes in capillary bed perfusion. A rationale for anecdotally reported sensor faults is also proposed based on the physical mechanisms explored.Item Modeling the clinical predictivity of palpitation symptom reports : mapping body cognition onto cardiac and neurophysiological measurements(2011-12) McNally, Robert Owen; Werner, Gerhard, 1921-; Harmon, Glynn; Demaris, David; Francisco-Revilla, Luis; Immroth, Barbara; Lokey, ScottThis dissertation models the relationship between symptoms of heart rhythm fluctuations and cardiac measurements in order to better identify the probabilities of either a primarily organic or psychosomatic cause, and to better understand cognition of the internal body. The medical system needs to distinguish patients with actual cardiac problems from those who are misperceiving benign heart rhythms due to psychosomatic conditions. Cognitive neuroscience needs models showing how the brain processes sensations of palpitations. Psychologists and philosophers want data and analyses that address longstanding controversies about the validity of introspective methods. I therefore undertake a series of measurements to model how well patient descriptions of heartbeat fluctuations correspond to cardiac arrhythmias. First, I employ a formula for Bayesian inference and an initial probability for disease. The presence of particular phrases in symptom reports is shown to modify the probability that a patient has a clinically significant heart rhythm disorder. A second measure of body knowledge accuracy uses a corpus of one hundred symptom reports to estimate the positive predictive value for arrhythmias contained in language about palpitations. This produces a metric representing average predictivity for cardiac arrhythmias in a population. A third effort investigates the percentage of patients with palpitations report actually diagnosed with arrhythmias by examining data from a series of studies. The major finding suggests that phenomenological reports about heartbeats are as or are more predictive of clinically significant arrhythmias than non-introspection-based data sources. This calculation can help clinicians who must diagnose an organic or psychosomatic etiology. Secondly, examining a corpus of reports for how well they predict the presence of cardiac rhythm disorders yielded a mean positive predictive value of 0.491. Thirdly, I reviewed studies of palpitations reporters, half of which showed between 15% and 26% of patients had significant or serious arrhythmias. In addition, evidence is presented that psychosomatic-based palpitation reports are likely due to cognitive filtering and processing of cardiac afferents by brainstem, thalamic, and cortical neurons. A framework is proposed to model these results, integrating neurophysiological, cognitive, and clinical levels of explanation. Strategies for developing therapies for patients suffering from identifiably psychosomatic-based palpitations are outlined.