Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: Systems and methods mediate anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and dispose of identified health anomalies. Multiple detection algorithms, operating asynchronously and independently, run against one or more health data streams. Examples of data streams are electronic laboratory requisitions and results, OTC sales of medicines and medical supplies, emergency department visit data, and others. The outputs of anomaly detection generators—anomaly notifications (anomalies)—are processed by the invention. The case manager organizes anomaly notifications and supports collaborative decision making and disposition among expert users.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise. A network server stores health data that has been gathered from various sources. A user can visualize the data on a client computer networked to the server by downloading from the server user specified sets of health-related data based on a case or set of anomalies describing a subset of the data, and source code for visualizing the data.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: A method and apparatus for anomaly detection in a data stream are disclosed. In one embodiment, the present method detects an anomalous condition in a data stream, by calculating at least one expected base event count for at least one event in the data stream for a time interval, obtaining an actual event count for the at least one event in the data stream, applying at least one shrinkage factor to the at least one expected base event count to obtain at least one actual estimated event count and detecting the anomalous condition in accordance with the actual event count and the at least one actual estimated event count.

Abstract: A system and method for mediating anomaly notifications in health data to health alerts using data structures and logic to organize, contain, and disposition identified health anomalies. Multiple generators of anomaly notifications operating asynchronously and independently can be processed. The case manager organizes anomaly notifications and supports collaborative decision making among users with diverse areas of expertise.

Abstract: Disclosed herein is a multi-domain anomaly pattern definition and detection module. The module receives raw data from different kinds of anomalies from a variety of detection algorithms and generates scores associated with the data. If any scores exceed a threshold, the algorithm gathers further information such as counts or listings of detailed data for a geographic region. The detailed data can include emergency department and lab department data related to a particular health concern such as a respiratory syndrome. Summaries can identify anomalies and numbers of events according to geographic region and utilizing probability algorithms. Other databases such as animal data collected under the Department of Agriculture may also be utilized. The data is presented in a familiar form such as a map or a table such that a subject matter expert may determine whether to further investigate an anomaly as a potential risk, for example, a health risk.

Abstract: Disclosed herein is a multi-domain anomaly pattern definition and detection module. The module receives raw data from different kinds of anomalies from a variety of detection algorithms and generates scores associated with the data. If one or more scores exceed a threshold, then the algorithm gathers further information which may include counts or listings of detailed data for a geographic region which may include such information as emergency department and lab department data related to a particular health concern such as a respiratory syndrome. Summaries are provided which may identify anomalies and numbers of events according to geographic region and utilizing probability algorithms. Other databases such as animal data collected under the Department of Agriculture may also be utilized. The data is presented in a familiar form such as a map or a table such that a subject matter expert may determine whether to further investigate an anomaly as a potential risk, for example, a health risk.

Abstract: A method and a processing device are provided for forecasting whether a record of data for an individual is associated with an outcome. Whether an n-gram associated with one of a group of predictors matches an n-gram included in the record of data is determined. When the n-gram included with the one of the group of predictors matches the n-gram in the record of data, a prediction is made as to whether the record of data is associated with the outcome based on a value assigned to the one of the group of predictors.

Abstract: A method and a processing device are provided for forecasting whether a record of data for an individual is associated with an outcome. Whether an n-gram associated with one of a group of predictors matches an n-gram included in the record of data is determined. When the n-gram included with the one of the group of predictors matches the n-gram in the record of data, a prediction is made as to whether the record of data is associated with the outcome based on a value assigned to the one of the group of predictors.

Abstract: A method and a device are provided for forecasting a volume associated with an outcome. A determination is made with respect to whether n-grams associated with a group of predictors matches n-grams included in records of data. A probability is estimated with respect to each of the records of data being associated with the outcome based on a value assigned to a respective one of the group of predictors when the n-gram associated with the respective one of the group of predictors is determined to match the n-gram included in a respective one of the records of data. An estimate of the volume associated with the outcome is calculated based on the estimated probabilities.

Abstract: A method and a processing device are provided for forecasting whether a record of data for an individual is associated with an outcome. Whether an n-gram associated with one of a group of predictors matches an n-gram included in the record of data is determined. When the n-gram included with the one of the group of predictors matches the n-gram in the record of data, a prediction is made as to whether the record of data is associated with the outcome based on a value assigned to the one of the group of predictors.

Abstract: A multi-user vehicle telemetric system comprises vehicle interface units (VIUs), wireless gateways, and one or more central hosts. The VIU in a vehicle collects data over the OBD-II bus and stores the data in the form of DataPoint Records (DPRs) in an on-board non-volatile (e.g. flash) memory. When the VIU is within wireless range of a gateway, it establishes a WiFi (802.11b) connection with the gateway, and submits stored DPRs to the gateway, to be stored in permanent storage at the gateway. Although the DPRs are stored in permanent storage on the gateway, they are deleted from permanent storage on the gateway after they are successfully uploaded to the central hosts. The gateways are shared, and communicate with the central hosts over a wide area network (WAN). When a gateway has gathered new DPRs from a VIU, it submits these to selected central hosts.

Abstract: A vehicle telemetric system comprises vehicle interface units (VIUs), wireless gateways, and a central host. The VIU in a vehicle collects data over the OBD-II bus and stores the data in the form of Data Point Records (DPRs) in an on-board flash memory. When the VIU is within wireless range of a gateway, it establishes a WiFi (802.11b) connection with the gateway, and submits stored DPRs to the gateway, to be stored in permanent storage at the gateway. The gateways communicate with the central host over a wide area network (WAN). When a gateway has gathered new DPRs from a VIU, it submits these to the central host. Databases in the gateways as well as in the central host are maintained and synchronized to track received DPRs by sequence number and originating VIU. In conjunction with specific protocols, all DPRs are thus collected reliably, even though communication with a vehicle may be intermittent. Efficient use of WiFi bandwidth is made by avoiding the unnecessary collection of duplicate DPRs.