William Elazmeh

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This paper presents a privacy compliance engine that monitors emails generated in an organization for violation of privacy policy of this organization. Our architecture includes four components: a domain knowledge defining the entities and private information we are dealing with, a pre-analysis component that extracts header information and segments the(More)
Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and(More)
Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable. We propose a generic framework for classifier evaluation to(More)
Privacy is one of the main societal concerns raised by critics of the uncontrolled growth and spread of information technology in developed societies. The purpose of this paper is to propose a privacy compliance engine that takes email messages as input and filters those that violate the privacy rules of the organization in which it is deployed. Our system(More)
The paper presents ongoing issues, challenges, and difficulties we face in applying machine learning methods to retrospectively collected clinical data. The objective of our research is to build a reliable prediction model for early assessment of emergency pediatric asthma exacerbations. This predictive model should be able to distinguish between patients(More)
Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify the confident segment of an ROC curve.(More)
Breaching information privacy is a critical problem where legal remedies intervene only after the fact rather than prevent it. This paper presents an organizational privacy compliance engine that monitors outgoing emails to detect breaches of a privacy policy in an organization. The PEEP system employs email content analysis techniques to extract(More)
held Sunday and Monday, July 22–23, in Vancouver, British Columbia, Canada. The program included the following thirteen workshops: (1) Acquiring Planning Knowledge via Demonstration; (2) Configuration; (3) Evaluating Architectures for Intelligence; (4) Evaluation Methods for Machine Learning; (5) ExplanationAware Computing; (6) Human Implications of(More)
Evaluating classifiers with increased confidence can significantly impact the success of many machine learning applications. However, traditional machine learning evaluation measures fail to provide any levels of confidence in their results. In this paper, we motivate the need for confidence in classifier evaluation at a level suitable for medical studies.(More)