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

Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

Using linear support vector machines, we investigated the feature selection problem for the application of all-against-all classification of a set of 20 chemicals using two types of sensors, classical doped tin oxide and zeolite-coated chromium titanium oxide sensors. We defined a simple set of possible features, namely the identity of the sensors and the sampling times and tested all possible combinations of such features in a wrapper approach. We confirmed that performance is improved, relative to previous results using this data set, by exhaustive comparison of these feature sets. Using the maximal number of different sensors and all available data points for each sensor does not necessarily yield the best results, even for the large number of classes in this problem. We contrast this analysis, using exhaustive screening of simple feature sets, with a number of more complex feature choices and find that subsampled sets of simple features can perform better. Analysis of potential predictors of classification performance revealed some relevance of clustering properties of the data and of correlations among sensor responses but failed to identify a single measure to predict classification success, reinforcing the relevance of the wrapper approach used. Comparison of the two sensor technologies showed that, in isolation, the doped tin oxide sensors performed better than the zeolite-coated chromium titanium oxide sensors but that mixed arrays, combining both technologies, performed best

Malignant Struma Ovarii: A Case Report

We present a case of a 40-yr-old woman diagnosed with a primary malignant struma ovarii. The patient was admitted with the complaint of pelvic pain and a large pelvic mass in the mid-portion of lower abdomen on gynecological examination. Pre-operative tumor markers and routine biochemistry were unremarkable. She was treated with total abdominal hysterectomy and right salpingo-oopherectomy. Post-operatively, she was diagnosed with a malignant struma ovarii through the usage of histopathological criteria similar to the guidelines for primary thyroid gland disease. The patient was subsequently performed left salpingo-oopherectomy and retroperitoneal pelvic lympadenectomy for re-staging. Although, left ovary and lymph nodes were histopathologically normal, she was offered thyroidectomy but she refused to accept the offer. Thyroglobulin level was monitored in the post-operative period. She is free of the disease for 18 months

Emission Characteristics and Factors of Selected Odorous Compounds at a Wastewater Treatment Plant

This study was initiated to explore the emission characteristics of Reduced Sulfur Compounds (RSCs: hydrogen sulfide, methyl mercaptan, dimethyl sulfide, dimethyl disulfide), ammonia and trimethylamine from a Wastewater Treatment Plant (WWTP) located at Sun-Cheon, Chonlanam-Do in South Korea. The study also evaluates flux profiles of the six selected odorous compounds and their flux rates (μg/m2/min) and compares their emission characteristics. A Dynamic Flux Chamber DFC was used to measure fluxes of pollutants from the treatment plant. Quality control of odor samples using a non-reactive sulfur dioxide gas determined the time taken for DFC concentration to reach equilibrium. The reduced sulfur compounds were analyzed by interfacing gas chromatography with a Pulsed Flame Photometric Detector (PFPD). Air samples were collected in the morning and afternoon on one day during summer (August) and two days in winter (December and January). Their emission rates were determined and it was observed that during summer relatively higher amounts of the selected odorous compounds were emitted compared to winter. Air samples from primary settling basin, aeration basin, and final settling basin were tested and the total amount of selected odorous compounds emitted per wastewater ton was found to be 1344 μg/m3 from the selected treatment processes. It was also observed that, in this study, the dominant odor intensity contribution was caused by dimethyl disulfide (69.1%)

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Suitability of PSA-detected localised prostate cancers for focal therapy: Experience from the ProtecT study

This article is available through a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported License. Copyright @ 2011 Cancer Research UK.Background: Contemporary screening for prostate cancer frequently identifies small volume, low-grade lesions. Some clinicians have advocated focal prostatic ablation as an alternative to more aggressive interventions to manage these lesions. To identify which patients might benefit from focal ablative techniques, we analysed the surgical specimens of a large sample of population-detected men undergoing radical prostatectomy as part of a randomised clinical trial. Methods: Surgical specimens from 525 men who underwent prostatectomy within the ProtecT study were analysed to determine tumour volume, location and grade. These findings were compared with information available in the biopsy specimen to examine whether focal therapy could be provided appropriately. Results: Solitary cancers were found in prostatectomy specimens from 19% (100 out of 525) of men. In addition, 73 out of 425 (17%) men had multiple cancers with a solitary significant tumour focus. Thus, 173 out of 525 (33%) men had tumours potentially suitable for focal therapy. The majority of these were small, well-differentiated lesions that appeared to be pathologically insignificant (38–66%). Criteria used to select patients for focal prostatic ablation underestimated the cancer's significance in 26% (34 out of 130) of men and resulted in overtreatment in more than half. Only 18% (24 out of 130) of men presumed eligible for focal therapy, actually had significant solitary lesions. Conclusion: Focal therapy appears inappropriate for the majority of men presenting with prostate-specific antigen-detected localised prostate cancer. Unifocal prostate cancers suitable for focal ablation are difficult to identify pre-operatively using biopsy alone. Most lesions meeting criteria for focal ablation were either more aggressive than expected or posed little threat of progression.National Institute for Health Researc