Abstract: In some implementations, a computing device can be configured to perform impact-based fraud detection. For example, a computing device (e.g., a network server) can receive user submissions from user devices corresponding to traffic incident reports, point of interest (POI) ratings, product ratings, vendor ratings, and/or other crowd-sourced information. The computing device can identify groups of users based on the user submissions and various grouping criteria. The computing device can determine the impact of these user groups with respect to the targets (e.g., traffic in an area, ratings of a business, etc.) of their user submissions. The computing device can prioritize high impact user groups when attempting to detect fraudulent user group activity (e.g., fraud rings).

Abstract: In some implementations, a computing device can be configured to perform impact-based fraud detection. For example, a computing device (e.g., a network server) can receive user submissions from user devices corresponding to traffic incident reports, point of interest (POI) ratings, product ratings, vendor ratings, and/or other crowd-sourced information. The computing device can identify groups of users based on the user submissions and various grouping criteria. The computing device can determine the impact of these user groups with respect to the targets (e.g., traffic in an area, ratings of a business, etc.) of their user submissions. The computing device can prioritize high impact user groups when attempting to detect fraudulent user group activity (e.g., fraud rings).

Abstract: A decision support system is provided for the management of extubation in intensive care unit patients. Based on multi-organ variability analysis of physiological signals, the proposed system transforms acquired waveforms into clinical information such as the risk of failing extubation and the probability of passing extubation. Furthermore, a variety of mechanisms are provided for displaying the extracted information to support a clinician's decisions.

Abstract: A decision support system is provided for the management of extubation in intensive care unit patients. Based on multi-organ variability analysis of physiological signals, the proposed system transforms acquired waveforms into clinical information such as the risk of failing extubation and the probability of passing extubation. Furthermore, a variety of mechanisms are provided for displaying the extracted information to support a clinician's decisions.

Abstract: A system and method are provided for assessing quality for a variability analysis. The method comprises: obtaining at least one waveform corresponding to a corresponding physiological measurement; determining at least one measure of waveform quality of the at least one waveform; extracting from a waveform, at least one event time series; determining a measure of event time series quality of the at least one event time series; determining at least one measure of stationarity of the at least one event time series; computing a quality measure using the at least one measure of waveform quality and the at least one measure of stationarity; and displaying the quality measure.

Abstract: A system and method are provided for assessing the degree of critical illness of patients in Intensive Care Units (ICUs), and the detection of occurrence of specific clinical outcomes, pursued through the monitoring of a variety of physiological signals, extraction of multiple measures of variability, and integration of the relevant information the measures of variability bring. The integration process leads to the creation of a compound variable, referred to as a composite measure of variability, which can be tuned to various clinical needs, since the process of normalization, feature selection and data integration are customizable to the clinical indication, planned use of the composite variability monitoring. The system and method also enable joining the composite measure of variability with clinical factors, so as to estimate the likelihood of developing specific clinical outcomes.