A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.

A Support Vector Machine (SVM) is a popular tool for decision support. The traditional way to build an SVM model is to estimate parameters based on a centralized repository of data. However, in the field of biomedicine, patient data are sometimes stored in local repositories or institutions where they were collected, and may not be easily shared due to privacy concerns.

A Randomized Response Model For Privacy Preserving Smart Metering.

The adoption of smart meters may bring new privacy concerns to the general public. Given the fact that metering data of individual homes/factories is accumulated every 15 minutes, it is possible to infer the pattern of electricity consumption of individual users. In order to protect the privacy of users in a completely de-centralized setting (i.

Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic.

Background: Spatial outbreak detection algorithms using routinely collected healthcare data have been developed since the late 90s to identify and locate disease outbreaks. However, current well-received spatial algorithms assume only one outbreak cluster present at the same point of time which may not be valid during a pandemic when several clusters of geographic areas concurrently occur. Based on a retrospective evaluation on time-series and spatial algorithms, this paper suggests that time series analysis in detection of pandemics is still a desirable process, which may achieve more sensitive performance with better timeliness.

Rank-based spatial clustering: an algorithm for rapid outbreak detection.

Objective: Public health surveillance requires outbreak detection algorithms with computational efficiency sufficient to handle the increasing volume of disease surveillance data. In response to this need, the authors propose a spatial clustering algorithm, rank-based spatial clustering (RSC), that detects rapidly infectious but non-contagious disease outbreaks.

Design: The authors compared the outbreak-detection performance of RSC with that of three well established algorithms-the wavelet anomaly detector (WAD), the spatial scan statistic (KSS), and the Bayesian spatial scan statistic (BSS)-using real disease surveillance data on to which they superimposed simulated disease outbreaks.

Probabilistic case detection for disease surveillance using data in electronic medical records.

This paper describes a probabilistic case detection system (CDS) that uses a Bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. The diagnostic accuracy of CDS for these conditions, as measured by the area under the ROC curve, was 0.97, and the overall accuracy for NLP employed in CDS was 0.

A multi-level spatial clustering algorithm for detection of disease outbreaks.

In this paper, we proposed a Multi-level Spatial Clustering (MSC) algorithm for rapid detection of emerging disease outbreaks prospectively. We used the semi-synthetic data for algorithm evaluation. We applied BARD algorithm [1] to generate outbreak counts for simulation of aerosol release of Anthrax.

Timeliness study of radiology and microbiology reports in a healthcare system for biosurveillance.

We developed a framework to measure the timeliness of two data types--radiology and microbiology reports--for detection of diseases such as inhalational anthrax (IA) in a healthcare system. We measured the timeliness of a data type as the delay between patient registration in an emergency department (ED) and receipt of data type by a biosurveillance system. We also determined the lower and upper bounds of median delay time (LMDT and UMDT) for the two data types to be available for detection of a single IA case.