6423442
Rule-based anomaly pattern detection for detecting disease outbreaks
This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.
28503548
Early Outbreak Detection Using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory
Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.
14362967
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.
578051
Bayesian Biosurveillance of Disease Outbreaks
Early, reliable detection of disease outbreaks is a critical problem today. This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease (respiratory anthrax infection) in a population of people. The number of parameters in such a network can become enormous, if not carefully managed. Also, inference needs to be performed in real time as population data stream in. We describe techniques we have applied to address both the modeling and inference challenges. A key contribution of this paper is the explication of assumptions and techniques that are sufficient to allow the scaling of Bayesian network modeling and inference to millions of nodes for real-time surveillance applications. The results reported here provide a proof-of-concept that Bayesian networks can serve as the foundation of a system that effectively performs Bayesian biosurveillance of disease outbreaks.
32510848
Using Augmented Virtuality for Remote Collaboration
This paper describes the concept, prototypical implementation, and usability evaluation of the Augmented Virtuality (AV)-based videoconferencing (VC) system cAR/PE!. We present a solution that allows three participants at different locations to communicate over a network in an environment simulating a traditional face-to-face meeting. Integrated into the AV environment are live video streams of the participants spatially arranged around a virtual table, a large virtual presentation screen for 2D display and application sharing, and 3D geometry (models) within the room and on top of the table. We describe the general concept and application scenario as well as the actual hardware setup, the implementation, and the use of the system in its current state. Results of two usability studies with 87 subjects are presented that show the general usability of our approach as well as good overall satisfaction. Parts of the work described here were presented as a poster at the second International Symposium on Mixed and Augmented Reality (Regenbrecht, Ott, Wagner, Lum, Kohler, et al., 2003. An Augmented Virtuality Approach to 3D Videoconferencing. Poster at 2nd Int. Symp. on Mixed and Aug. Reality, Tokyo.).
30427509
Ambiguity reduction in speaker identification by the relaxation labeling process
A nonlinear probabilistic model of the relaxation labeling (RL) process is implemented in the speaker identification task in order to disambiguate the labeling of the speech feature vectors. In this proposed algorithm, the deterministic labeling of the vector quantization (VQ)-based speaker identification is relaxed by means of introducing initial probabilistic weights to the labeling process of the speech feature vectors. This process is then iteratively updated until no further significant improvement is found. Experimental results on speaker identification using a commercial speech corpus show that the relaxation labeling outperforms the conventional VQ method.
751960
Performance Evaluation Methodologies for Abstract Data Type Implementation Techniques
Abstract The runtime management system for a programming language must allow the efficient manipulation of complex objects, such as instances of abstract data types (ADTs). We describe an implementation model for ADTs based on coroutine objects (i.e. as an alternative to ad hoc modifications of the runtime stack). We evaluate the performances of variants of our implementation model using two different approaches: a benchmarking technique which monitors actual costs for test programs and a “pseudo-execution” technique which estimates costs based on idealized instruction timings. The results of our comparison indicate that the two forms of evaluation give very similar cost estimates, supporting the use of the (cheaper) pseudo-execution technique. The overall performance results identify applications in which our coroutine implementation model has definite strengths and weaknesses. Finally, our implementation approach is compared with modified stack implementations used in other ADT systems.
14972044
Service-Oriented Adaptation in Ubiquitous Computing Environments
Ubiquitous computing environments may offer dynamically discoverable services that can be utilized by adaptive applications to improve their functionality and quality of service. We have developed a general software development framework for context-aware adaptive applications. In this paper we present our novel modeling concepts that extend an existing component-based modeling approach by service-oriented adaptation concepts. With our new extensions a dynamically discovered service may replace an application component if it increases the overall utility of the application. We discuss open questions and point to potential solution approaches.
32953498
A geostatistical model for linear prediction analysis of speech
Abstract This paper presents a geostatistical model as a new approach to the linear prediction analysis of speech. The autocorrelation method of autoregressive modeling, which is widely applied in the linear predictive coding of speech, is used as a benchmark for comparison with the present algorithm. Before discussing the proposed model, we will briefly describe the concepts of linear prediction analysis of speech and how this is solved by the well-known method of autocorrelation. Following is the introduction of geostatistics including the ideas of regionalized variables, semi-variograms and kriging equations. We then propose a geostatistical model to the linear prediction modeling of speech signals. Examples on speech data are given to illustrate the effectiveness of the present algorithm in comparison with the autocorrelation method. Advantages offered by the proposed geostatistical algorithm over the autocorrelation method in the linear prediction analysis of speech are summarized as follows: (1) it is more effective due to the optimization of the kriging equations taking into account the biased condition; (2) it is more flexible by allowing different biased values for the fitting of the signal spectrum, and therefore may provide a means for adaptive LPC; (3) it can give a good estimate of the number of poles used in the LPC by means of the theoretical semi-variogram.