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STUDY OBJECTIVE The national standard for door-to-balloon time is 90 minutes, as recommended by the American Heart Association/American College of Cardiology guidelines for ST-elevation myocardial infarction (STEMI). Percutaneous coronary intervention for STEMI was initiated at our institution in June 2004. Review of our door-to-balloon times revealed that(More)
Congestive Heart Failure (CHF) is a serious chronic condition often leading to 50% mortality within 5 years. Improper treatment and post-discharge care of CHF patients leads to repeat frequent hospitalizations (i.e., readmissions). Accurately predicting patient's risk-of-readmission enables care-providers to plan resources, perform factor analysis, and(More)
The increasing availability of digital health records should ideally improve accountability in healthcare. In this context, the study of predictive modeling of healthcare costs forms a foundation for accountable care, at both population and individual patient-level care. In this research we use machine learning algorithms for accurate predictions of(More)
This work proposes a protocol for performing linear regression over a dataset that is distributed over multiple parties. The parties will jointly compute a linear regression model without actually sharing their own private datasets. We provide security definitions, a protocol, and security proofs. Our solution is information-theoretically secure and is(More)
STUDY OBJECTIVES To describe preoperative and postoperative sleep disruption and its relationship to postoperative delirium. DESIGN Prospective cohort study with 6 time points (3 nights pre-hospitalization and 3 nights post-surgery). SETTING University medical center. PATIENTS The sample consisted of 50 English-speaking patients ≥ 40 years of age(More)
Huge outlays of foreign aid to Africa have failed to help countries in the region develop. Africa lags the rest of the world in almost any measure of development. A growing scholarly consensus suggests the reasons foreign assistance has failed relate to the design of assistance programs. Studies show that policy preconditions are critical to the efficacy of(More)
—Many data-driven personalized services require that private data of users is scored against a trained machine learning model. In this paper we propose a novel protocol for privacy-preserving classification of decision trees, a popular machine learning model in these scenarios. Our solutions are composed out of building blocks, namely a secure comparison(More)
In this demonstration proposal we describe Health-SCOPE (Healthcare Scalable COst Prediction Engine), a frame-work for exploring historical and present day healthcare costs as well as for predicting future costs. Health SCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, Health SCOPE supports a population(More)
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