Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

We created an Eclipse plug-in called FrenchPress that offers students feedback on their Java programming style. It is designed not for novices but for students taking their second or third Java course. Advanced beginner students know enough to produce a program with the desired input/output behavior, but fail to understand it could still be poorly written. Large class sizes in introductory courses make it difficult for instructors to give their students individualized attention. FrenchPress automates a small subset of the feedback students might have received from educators. The system diagnoses issues characteristic of programmers who have not yet assimilated the object-oriented paradigm, such as misuse of the public modifier, fields that should have been local variables, and instance variables that should have been class constants. We conducted a classroom trial of the plug-in covering four assignments in data structures and algorithms. Among students whose code triggered one or more diagnostic rules, the percentage who modified their program in response to FrenchPress feedback varied from a high of 59% on the first project to a low of 23% on the second and fourth projects. On the user surveys following each assignment, 56-66% of respondents said they were satisfied with FrenchPress performance.

We created an Eclipse plug-in called FrenchPress that partially automates the task of giving students feedback on their Java programs. It is designed not for novices but for students taking their second or third Java course: students who know enough Java to write a working program but lack the judgment to recognize bad code when they see it. FrenchPress does not diagnose compile-time or runtime errors, or logical errors that produce incorrect output. It targets silent flaws, flaws the student is unable to identify for himself because nothing in the programming environment alerts him. FrenchPress diagnoses flaws characteristic of programmers who have not yet assimilated the object-oriented idiom. Such shortcomings include misuse of the public modifier, fields that should have been local variables, and instance variables that should have been class constants. Other rules address the all too common misunderstanding of the boolean datatype. FrenchPress delivers explanatory messages in a vocabulary appropriate to the student's current level.

FrenchPress is an Eclipse plug-in that partially automates the task of giving students feedback on their Java programs. It is designed not for novices but for students taking their second or third Java course: students who know enough Java to write a working program but lack the judgment to recognize bad code when they see it. FrenchPress does not diagnose compile-time or runtime errors, or logical errors that produce incorrect output. It targets silent flaws, flaws the student is unable to identify for herself because nothing in the programming environment alerts her.

Recent proposals to apply data mining systems to problems in law enforcement, national security, and fraud detection have attracted both media attention and technical critiques of their expected accuracy and impact on privacy. Unfortunately, the majority of technical critiques have been based on simplistic assumptions about data, classifiers, inference procedures, and the overall architecture of such systems. We consider these critiques in detail, and we construct a simulation model that more closely matches realistic systems. We show how both the accuracy and privacy impact of a hypothetical system could be substantially improved, and we discuss the necessary and sufficient conditions for this improvement to be achieved. This analysis is neither a defense nor a critique of any particular system concept. Rather, our model suggests alternative technical designs that could mitigate some concerns, but also raises more specific conditions that must be met for such systems to be both accurate and socially desirable.

In this project, w e dev eloped a technique for extracting useful information from databases that contain both xedformat and free-text elds.The present state of the art in data mining is a schism betw een tec hniques that handle only xed-format data (pattern recognition, classi cation algorithms from machine learning), and techniques designed for free-form text (information retrieval).Advanced knowledge disco very tec hnologies ha ve been developed in both research areas, but systems that can categorize or cluster records containing both kinds of data are still lacking.Speci cally, w e examined database records from a Honeywell service cen ter to extract information about the expected cost of dierent kinds of service requests.Our goal was to test the h ypothesis that incorporating information from free-text elds would provide a better categorization of these records; in this case, better predictions of the cost of the service call.In our w ork, we h a ve i n tegrated feature extraction and clustering techniques from information retrieval with classi cation algorithms from machine learning in order to categorize the hybrid elds.Our preliminary results suggested that incorporating free-form text could potentially induce better classi cation models.