2986922
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and weather. Creating the baseline distribution without taking these trends into account can lead to unacceptably high false positive counts and slow detection times. This paper replaces the baseline method of (Wong et al., 2002) with a Bayesian network which produces the baseline distribution by taking the joint distribution of the data and conditioning on attributes that are responsible for the trends. We show that our algorithm, called WSARE 3.0, is able to detect outbreaks in simulated data with almost the earliest possible detection time while keeping a low false positive count. We also include the results of running WSARE 3.0 on real Emergency Department data
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.
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.
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.
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.
32066141
A proposed fuzzy pattern verification system
Proposes a fuzzy pattern verification system based on fuzzy if-then rules and fuzzy membership functions in fuzzy clustering. The proposed fuzzy system is applied to speaker verification and is compared with current probabilistic verification systems. Experiments performed on the ANDOSL and YOHO speech corpora show better results for the proposed system.
61094772
Video to the rescue
Automatic person identity verification based on biometrics is a challenging problem, and has received much attention during recent years due to its many applications in on-line transaction processing, law enforcement, and security applications. However, most identity verification systems are primarily based on voice biometrics, and hence are more vulnerable to acoustic noise and channel distortions, in addition to train/test mismatch conditions. In this paper, we show how we can use video information to improve the performance of identity verification systems. The approach based on multimodal fusion of voice and face information from speaking face video allows robust identity verification performance. In addition, depending on the type of features and fusion technique used, it is also possible to perform liveness checks, allowing the system to detect fraudulent attacks on the system.
34651476
User verification through pointing characteristics: an exploration examination
Abstract Previous research has highlighted that all data security systems rest on the assumption of accurate user verification. Although much research has focused on speech and keystroke characteristics, no study has examined the potential of indirect cursor control devices, such as mouse or pen, as a means of personal user verification. This paper presents the result of an exploratory examination of the measurement of individual users' behaviour elicited while using two indirect input devices (puck and pen). Ten subjects undertook two series of 500 trials using the puck and pen in a counterbalanced design. Each trial involved guiding the cursor to one of ten possible targets, projected upon one of five angles of projection and one of two distances. Data recorded comprised a series of ( x, y) coordinates and associated time stamps of each component of the movement. Five parameters for each full movement were extracted-total time to reach the target, maximum velocity of the movement, maximum acceleration for the movement, time to maximum velocity of movement, time to maximum acceleration of the movement. Results of a series of Multivariate Analyses of Variance indicated that for both the puck and the pen data, there were statistically significant differences between subjects for each of the five parameters. This set of discriminating parameters was then embedded in a verification paradigm. Results indicated that the average error rates varied between 39% (S.D.=2%) and 14% (S.D.=2%) for the puck and 38% (S.D.=2%) and 12% (S.D.=2%) for the pen. Error rates were dependent upon the number of samples used when making the verification decision. This research suggests that pointing device measurements using the current parameters are not a viable method of user verification by themselves. However, with average error rates as low as 12%, it may be reasonable to integrate them into a multi-modal security system. Practical implications and future research directions are discussed.
7724289
Outpatient clinic scheduling - a simulation approach
The process by which outpatients are scheduled for a doctor's visit is a crucial determinant of the overall efficiency of the patient flow. The problem at hand consists of determining prioritization (triage) rules so that adequate patient care is guaranteed, resources (provider schedules) are utilized efficiently and a service guarantee can be ensured. We present a simulation framework for the evaluation and optimization of scheduling rules. We outline the basic ingredients of our model, illustrate the kinds of analyses it has enabled us to perform and summarize our experience with a preliminary implementation for the Division of Pediatric Ophthalmology at Cincinnati Children's Hospital Medical Center. Challenges for adaptations to other settings are also outlined.
10411388
Large-scale linear programming techniques for the design of protein folding potentials
Abstract.We present large-scale optimization techniques to model the energy function that underlies the folding process of proteins. Linear Programming is used to identify parameters in the energy function model, the objective being that the model predict the structure of known proteins correctly. Such trained functions can then be used either for ab-initio prediction or for recognition of unknown structures. In order to obtain good energy models we need to be able to solve dense Linear Programming Problems with tens (possibly hundreds) of millions of constraints in a few hundred parameters, which we achieve by tailoring and parallelizing the interior-point code PCx.