Strategic Level Proton Therapy Patient Admission Planning: A Markov Decision Process Modeling Approach

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI)

Quantifying Uncertainty in Deep Learning Classification with Noise in Discrete Inputs for Risk-Based Decision Making

The use of Deep Neural Network (DNN) models in risk-based decision-making has attracted extensive attention with broad applications in medical, finance, manufacturing, and quality control. To mitigate prediction-related risks in decision making, prediction confidence or uncertainty should be assessed alongside the overall performance of algorithms. Recent studies on Bayesian deep learning helps quantify prediction uncertainty arises from input noises and model parameters. However, the normality assumption of input noise in these models limits their applicability to problems involving categorical and discrete feature variables in tabular datasets. In this paper, we propose a mathematical framework to quantify prediction uncertainty for DNN models. The prediction uncertainty arises from errors in predictors that follow some known finite discrete distribution. We then conducted a case study using the framework to predict treatment outcome for tuberculosis patients during their course of treatment. The results demonstrate under a certain level of risk, we can identify risk-sensitive cases, which are prone to be misclassified due to error in predictors. Comparing to the Monte Carlo dropout method, our proposed framework is more aware of misclassification cases. Our proposed framework for uncertainty quantification in deep learning can support risk-based decision making in applications when discrete errors in predictors are present.Comment: 31 pages, 9 figure

Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management

This study presents two compensations methods for inventory discrepancy caused by demand, supply and lead time uncertainty as well as inventory related errors. The first method increases the resistance of supply chain by incrementing safety stock levels while the second method controls and corrects inventory discrepancy based on estimated errors. System-level errors are categorised and modelled under the best and the worst possible conditions to characterise and investigate the behaviour of discrepancy between on-hand and recorded inventory in a supply chain. Numerical analyses are performed to observe inventory and discrepancy behaviour to quantify the benefits of the methods. The results indicate that inventory errors can be characterised as an extra source of demand in supply chain. Incrementing the safety stocks based on inventory records can decrease stock-outs and lost sales but increase the level of on-hand inventory. Controlling and correcting inventory discrepancies, however, keeps low inventory

Identification of new drug candidates against Trichomonas gallinae using high-throughput screening

Trichomonas gallinae is a protozoan parasite that is the causative agent of trichomoniasis, and infects captive and wild bird species throughout the world. Although metronidazole has been the drug of choice against trichomoniasis for decades, most Trichomonas gallinae strains have developed resistance. Therefore, drugs with new modes of action or targets are urgently needed. Here, we report the development and application of a cell-based CCK-8 method for the high-throughput screening and identification of new inhibitors of Trichomonas gallinae as a beginning point for the development of new treatments for trichomoniasis. We performed the high-throughput screening of 173 anti-parasitic compounds, and found 16 compounds that were potentially effective against Trichomonas gallinae. By measuring the median inhibitory concentration (IC50) and median cytotoxic concentration (CC50), we identified 3 potentially safe and effective compounds against Trichomonas gallinae: anisomycin, fumagillin, and MG132. In conclusion, this research successfully established a high-throughput screening method for compounds and identified 3 new safe and effective compounds against Trichomonas gallinae, providing a new treatment scheme for trichomoniasis

Received: 15 May 2013; in revised form: 11 July 2013 / Accepted: 15 July 2013 / Published: 19 July 2013

Uveal melanoma (UM) is the most common primary intraocular malignancy and the leading potentially fatal primary intraocular disease in adults. Melanoma antigen recognized by T-cells (MART-1) has been studied extensively as a clinically important diagnostic marker for melanoma, however, its biological function remains unclear. In the present study, the UM cell line SP6.5, which showed a high level of MART-1 expression, was subjected to small interfering RNA-mediated silencing of MART-1. Silencing of MART-1 expression increased the migration ability of SP6.5 cells and down-regulated the expression of the metastasis suppressor NM23. Our results suggest that MART-1 is a candidate target for the development of therapeutic strategies for UM and in particular for the suppression of metastasis associated with this malignancy