An anomaly detection method for identifying locations with abnormal behavior of temperature in school buildings.

Time series data collected using wireless sensors, such as temperature and humidity, can provide insight into a building's heating, ventilation, and air conditioning (HVAC) system. Anomalies of these sensor measurements can be used to identify locations of a building that are poorly designed or maintained. Resolving the anomalies present in these locations can improve the thermal comfort of occupants, as well as improve air quality and energy efficiency levels in that space.

Minimizing features while maintaining performance in data classification problems.

High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible.

Discovering Long COVID Symptom Patterns: Association Rule Mining and Sentiment Analysis in Social Media Tweets.

Background: The COVID-19 pandemic is a substantial public health crisis that negatively affects human health and well-being. As a result of being infected with the coronavirus, patients can experience long-term health effects called long COVID syndrome. Multiple symptoms characterize this syndrome, and it is crucial to identify these symptoms as they may negatively impact patients' day-to-day lives.

Temperature clusters in commercial buildings using k-means and time series clustering.

An efficient building should be able to control its internal temperature in a manner that considers both the building's energy efficiency and the comfort level of its occupants. Thermostats help to control the temperature within a building by providing real-time data on the temperature inside that space to determine whether it is within the acceptable range of that building's control system, and proper thermostat placement helps to better control a building's temperature. More thermostats can provide better control of a building, as well as a better understanding of the building's temperature distribution.

Assessing the impact of the density and sparsity of the network on community detection using a Gaussian mixture random partition graph generator.

Identification of sub-networks within a network is essential to understand the functionality of a network. This process is called as 'Community detection'. There are various existing community detection algorithms, and the performance of these algorithms can be varied based on the network structure.

Model-based estimation of baseball batting metrics.

We introduce an approach to model the batting outcomes of baseball batters based on the weighted likelihood approach and make use of our methodology to estimate commonly used baseball batting metrics. The weighted likelihood allows the sharing of relevant information among players. Specifically, this allows the inference on each batter to make use of the batting data from all other players in the league and, in the process, allows for improved inference.

Bayesian Equivalence Testing and Meta-Analysis in Two-Arm Trials with Binary Data.

We consider a Bayesian approach for assessing hypotheses of equivalence in two-arm trials with binary Data. We discuss the development of likelihood, the prior, and the posterior distributions of parameters of interest. We then examine the suitability of a normal approximation to the posterior distribution obtained via a Taylor series expansion.

Model-based methods for case definitions from administrative health data: application to rheumatoid arthritis.

Objective: This research proposes a model-based method to facilitate the selection of disease case definitions from validation studies for administrative health data. The method is demonstrated for a rheumatoid arthritis (RA) validation study.

Study Design And Setting: Data were from 148 definitions to ascertain cases of RA in hospital, physician and prescription medication administrative data.

Identifying Distinct Healthcare Pathways During Episodes of Chronic Obstructive Pulmonary Disease Exacerbations.

Healthcare pathways are important to measure because they are expected to affect outcomes. However, they are challenging to define because patients exhibit heterogeneity in their use of healthcare services. The objective of this study was to identify and describe healthcare pathways during episodes of chronic obstructive pulmonary disease (COPD) exacerbations.

Bayesian inference in two-arm trials using relative belief ratios.

Various methodologies proposed for some inference problems associated with two-arm trails are known to suffer from difficulties, as documented in Senn (2001). We propose an alternative Bayesian approach to these problems that deals with these difficulties through providing an explicit measure of statistical evidence and the strength of this evidence. Bayesian methods are often criticized for their intrinsic subjectivity.

Meta-analysis using Dirichlet process.

This article develops a Bayesian approach for meta-analysis using the Dirichlet process. The key aspect of the Dirichlet process in meta-analysis is the ability to assess evidence of statistical heterogeneity or variation in the underlying effects across study while relaxing the distributional assumptions. We assume that the study effects are generated from a Dirichlet process.

A generalized p-value approach for assessing noninferiority in a three-arm trial.

The existing generalized p-value approach, from statistical literature, is applied to assess noninferiority of an experimental treatment in a three-arm clinical trial including a placebo. Two generalized test functions (GTFs) are constructed and Monte Carlo simulations are used to compute the p-value. The GTFs perform well in terms of maintaining the Type-I error probabilities, and the power of the tests are shown to increase to 1 as both the sample size and the parameter denoting the fraction of the effect of the reference drug with respect to placebo increase.