Journal of Data Science

Physician performance is critical to caring for patients admitted to the intensive care unit (ICU), who are in life-threatening situations and require high level medical care and interventions. Evaluating physicians is crucial for ensuring a high standard of medical care and fostering continuous performance improvement. The non-randomized nature of ICU data often results in imbalance in patient covariates across physician groups, making direct comparisons of the patients’ survival probabilities for each physician misleading. In this article, we utilize the propensity weighting method to address confounding, achieve covariates balance, and assess physician effects. Due to possible model misspecification, we compare the performance of the propensity weighting methods using both parametric models and super learning methods. When the generalized propensity or the quality function is not correctly specified within the parametric propensity weighting framework, super learning-based propensity weighting methods yield more efficient estimators. We demonstrate that utilizing propensity weighting offers an effective way to assess physician performance, a topic of considerable interest to hospital administrators.

This paper introduces the package open-crypto for free-of-charge and systematic cryptocurrency data collecting. The package supports several methods to request (1) static data, (2) real-time data and (3) historical data. It allows to retrieve data from over 100 of the most popular and liquid exchanges world-wide. New exchanges can easily be added with the help of provided templates or updated with build-in functions from the project repository. The package is available on GitHub and the Python package index (PyPi). The data is stored in a relational SQL database and therefore accessible from many different programming languages. We provide a hands-on and illustrations for each data type, explanations on the received data and also demonstrate the usability from R and Matlab. Academic research heavily relies on costly or confidential data, however, open data projects are becoming increasingly important. This project is mainly motivated to contribute to openly accessible software and free data in the cryptocurrency markets to improve transparency and reproducibility in research and any other disciplines.

Abstract: Quick identification of severe injury crashes can help Emergency Medical Services (EMS) better allocate their scarce resources to improve the survival of severely injured crash victims by providing them with a fast and timely response. Data broadcast from a vehicle’s Event Data Recorder (EDR) provide an opportunity to capture crash information and send them to EMS near real-time. A key feature of EDR data is a longitudinal measure of crash deceleration. We used functional data analysis (FDA) to ascertain key features of the deceleration trajectories (absolute integral, absolute in- tegral of its slope, and residual variance) to develop and verify a risk predic- tion model for serious (AIS 3+) injuries. We used data from the 2002-2012 EDR reports and the National Highway and National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) datasets available on the National Transportation Safety Administration (NHTSA) website. We consider a variety of approaches to model deceleration data, including non- penalized and penalized splines and a variable selection method, ultimately obtaining a model with a weighted AUC of 0.93. A novel feature of our approach is the use of residual variance as a measure of predictive risk. Our model can be viewed as an important first step towards developing a real- time prediction model capable of predicting the risk of severe injury in any motor vehicle crash.

Abstract: Identification of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specification of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.

Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics & statistics. In today’s data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment. MatLab can be used to teach introductory mathematics such as calculus and statistics. Both Python and R can be used to make decisions involving big data. On the one hand, Python is perfect for teaching introductory statistics in a data rich environment. On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.