Approximate dynamic programming for anemia management.

The focus of this dissertation work is the formulation and improvement of anemia management process involving trial-and-error. A two-stage method is adopted toward this objective. Given a medical treatment process, a discrete Markov representation is first derived as a formal translation of the treatment process to a control problem under uncertainty. A simulative numerical solution of the control problem is then obtained on-the-fly in the form of a control law maximizing the long-term benefit at each decision stage. Approximate dynamic programming methods are employed in the proposed solution. The motivation underlying this choice is that, in reality, some patient characteristics, which are critical for the sake of treatment, cannot be determined through diagnosis and remain unknown until early stages of treatment, when the patient demonstrates them upon actions by the decision maker. A review of these simulative control tools, which are studied extensively in reinforcement learning theory, is presented. Two approximate dynamic programming tools, namely SARSA and Q -learning, are introduced. Their performance in discovering the optimal individualized drug dosing policy is illustrated on hypothetical patients made up as fuzzy models for simulations. As an addition to these generic reinforcement learning methods, a state abstraction scheme for the considered application domain is also proposed. The control methods of this study, capturing the essentials of a drug delivery problem, constitutes a novel computational framework for model-free medical treatment. Experimental evaluation of the dosing strategies produced by the proposed methods against the standard policy, which is being followed actually by human experts in Kidney Diseases Program, University of Louisville, shows the advantages for use of reinforcement learning in the drug dosing problem in particular and in medical decision making in general

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Rethinking Knowledge and Pedagogy in Dental Education

Dentistry as a profession has often been considered both art and science. Traditional dental education has attempted to address both; however, in many places only the science of dentistry is emphasized. The move toward competency-based curricula in dental education requires an expansion of what constitutes meaningful knowledge in the curriculum and what pedagogies best support that curriculum. The scientific and technical knowledge considered foundational to clinical practice is not sufficient to teach competencies associated with the art of dentistry. Habermas, a social scientist, offers a way of looking beyond technical knowledge to consider two other forms of knowledge: practical and emancipatory. Pedagogy that supports development of practical and emancipatory knowledge includes problem-based learning and case methods, heuristics, reflective practica, journals, storytelling, and performance-based assessment methods. These important teaching strategies are being integrated into various dental curricula including a new competency-based dental curriculum at Marquette University\u27s School of Dentistry. It will be critical for dental educators to continue developing these methods to provide efficient and effective education for future practitioners in both the art and science of dentistry

Building bridges between doctors and patients: the design and pilot evaluation of a training session in argumentation for chronic pain experts

Shared decision-making requires doctors to be competent in exchanging views with patients to identify the appropriate course of action. In this paper we focus on the potential of a course in argumentation as a promising way to empower doctors in presenting their viewpoints and addressing those of patients. Argumentation is the communication process in which the speaker, through the use of reasons, aims to convince the interlocutor of the acceptability of a viewpoint. The value of argumentation skills for doctors has been addressed in the literature. Yet, there is no research on what a course on argumentation might look like. In this paper, we present the content and format of a training session in argumentation for doctors and discuss some insights gained from a pilot study that examined doctors' perceived strengths and limitations vis-à-vis this training