Variable Ticket Pricing in Major League Baseball

Sport teams historically have been reluctant to change ticket prices during the season. Recently, however, numerous sport organizations have implemented variable ticket pricing in an effort to maximize revenues. In Major League Baseball variable pricing results in ticket price increases or decreases depending on factors such as quality of the opponent, day of the week, month of the year, and for special events such as opening day, Memorial Day, and Independence Day. Using censored regression and elasticity analysis, this article demonstrates that variable pricing would have yielded approximately $590,000 per year in additional ticket revenue for each major league team in 1996, ceteris paribus. Accounting for capacity constraints, this amounts to only about a 2.8$1.4 million in revenue. The largest percentage revenue gain would have been the San Francisco Giants. The Giants would have seen an estimated 6.7% increase in revenue had they used optimal variable pricing

Collaborative data mining for clinical trial analytics

Clinical research and drug development trials generate large amounts of data. Due to the dispersed nature of clinical trial data across multiple sites and heterogeneous databases, it remains a challenge to harness these trial data for analytics to gain more understanding about the implementation of studies as well as disease processes. Moreover, the veracity of the results from analytics is difficult to establish in such datasets. We make a two-fold contribution in this paper: First, we provide a mechanism to extract task-relevant data using Master Data Management (MDM) from a clinical trial database with data spread over several domain datasets. Second, we provide a method for validating findings by collaborative utilization of multiple data mining techniques, namely: classification, clustering, and association rule mining Overall, our approach aims at extracting useful knowledge from data collected during clinical trials to enable the development of faster and cheaper clinical trials that more accurate and impactful. For a demonstration of the efficacy of our proposed methods, we utilized the following datasets: (1) the National Institute on Drug Abuse (NIDA) data share repository and (2) the data from the Osteoarthritis initiative (OAI), where we found real-world implications in validating the findings using multiple data mining methods in a collaborative manner. The comparative results with existing state of the art techniques show the usefulness and high accuracy of our methods

Prediction of Cognitive Test Scores from Variable Length Multimodal Data in Alzheimer's Disease

Alzheimer's disease (AD) is a neurogenerative condition characterized by sharp cognitive decline with no confirmed effective treatment or cure. This makes it critically important to identify the symptoms of Alzheimer's disease in its early stages before significant cognitive deterioration has taken hold and even before any brain morphology and neuropathology are noticeable. In this study, five different multimodal deep neural networks (MDNN), with different architectures, in search of an optimal model for predicting the cognitive test scores for the Mini-Mental State Examination (MMSE) and the modified Alzheimer's Disease Assessment Scale (ADAS-CoG13) over a span of 60 months (5 years). The multimodal data utilized to train and test the proposed models were obtained from the Alzheimer's Disease Neuroimaging Initiative study and includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from the neuropsychological tests (Cog). The models developed herein delve into two main issues: (1) application merits of single-task vs. multitask for predicting future cognitive scores and (2) whether time-varying input data are better suited than specific timepoints for optimizing prediction results. This model yields a high of 90.27% (SD = 1.36) prediction accuracy (correlation) at 6 months after the initial visit to a lower 79.91% (SD = 8.84) prediction accuracy at 60 months. The analysis provided is comprehensive as it determines the predictions at all other timepoints and all MDNN models include converters in the CN and MCI groups (CNc, MCIc) and all the unstable groups in the CN and MCI groups (CNun and MCIun) that reverted to CN from MCI and to MCI from AD, so as not to bias the results. The results show that the best performance is achieved by a multimodal combined single-task long short-term memory (LSTM) regressor with an input sequence length of 2 data points (2 visits, 6 months apart) augmented with a pretrained Neural Network Estimator to fill in for the missing values