72 results about "Retrospective analysis" patented technology

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

Systems and methods for processing sensor analyte data, including initiating calibration, updating calibration, evaluating clinical acceptability of reference and sensor analyte data, and evaluating the quality of sensor calibration. During initial calibration, the analyte sensor data is evaluated over a period of time to determine stability of the sensor. The sensor may be calibrated using a calibration set of one or more matched sensor and reference analyte data pairs. The calibration may be updated after evaluating the calibration set for best calibration based on inclusion criteria with newly received reference analyte data. Fail-safe mechanisms are provided based on clinical acceptability of reference and analyte data and quality of sensor calibration. Algorithms provide for optimized prospective and retrospective analysis of estimated blood analyte data from an analyte sensor.

The present invention relates to a method to measure, record, analyze, and report cumulative radiation exposure to the patient population and provide automated feedback and recommendations to ordering clinicians and consultant radiologists. The data provided from this “radiation scorecard” would in turn be automatically recorded into a centralized data repository (radiation database), which would be independent to the acquisition site, technology employed, and individual end-user. Retrospective analysis can also be performed using a set of pre-defined scorecard data points tied to the individual patient's historical medical imaging database, thereby allowing for comprehensive (both retrospective and prospective) medical radiation exposure quantitative analysis. Patient safety can be improved by a combination of radiation dose reduction, exposure optimization, rigorous equipment quality control (QC), education and training of medical imaging professionals, and integration with computerized physician order entry (CPOE).

The invention discloses a warning video network storage method for a video monitoring system, which includes the following steps that: users configure warning video parameters on a configuration manager; a monitoring front-end reports a warning incident to the configuration manager when the warning incident is detected, the configuration manager sends a warning video command to a media server, the media server receives monitoring data from a monitoring front-end and writes it into a storage device, when the configuration manager sends a stop-videoing command, the media server stops receiving monitoring data and writing it into the storage device, and a warning video is generated. The invention also discloses a video monitoring system. The invention realizes the network storage for the front-end warning video of a carrier-class monitoring system on the media server, which effectively enhances the reliability and usability of monitoring video, provides a strong basis for alarm retrospective analysis, and obviously reduces the pre-investment of operators and the maintenance cost of system.

The invention discloses a network attack retrospective analysis method and network security equipment. The network attack retrospective analysis method comprises the steps that the network security equipment collects multidimensional historical network logs; when a network attack is detected, the network log is acquired, retrospective analysis is performed according to the network log and the historical network logs, and network attack data are acquired according to the result of retrospective analysis; and the network attack data are compiled as a safety event corresponding to the network attack. The accurate network attack data can be automatically acquired so that analysis difficulty of the network security event can be greatly reduced and great conditions can be provided for network security protection.

A method and system are provided for analyzing electromagnetic brain signals such as EEG and ECoG signals in a subject in real time and which avoids the need for time-intensive retrospective analysis of brain activity in the subject. This can be applied to all complex systems with multiple fluctuating signals to identify and predict significant events

The invention relates to a method for restarting and restoring work of a charging gateway (CG), which comprises the following steps : after failing and being restarted, the CG judges different conditions when the fault occurs, and respectively executing corresponding operation according to the information stored at a working area before the fault occurs; and under the condition that the work can not restored by a fault-tolerant mechanism between SGSN or GGSN, firstly, reasonably selecting the retrospective analyses time starting from the time point when the fault occurs, then carrying out retrospective comparative analysis on an original ticket and a final ticket locally stored by the CG, reconstructing an original ticket Hash table after discovering the uncombined original ticket and the final ticket and deleting the combined original ticket, and restoring normal work of the CG. The method comprises five operation steps of restarting the fault, judging the type of the fault, analyzing part of the ticket, reconstructing the Hash table and restoring the normal operation. The method leads the CG to be capable of completing restarting and restoring the previous work when any fault occurs, improves work reliability and the accuracy of the charging ticket, and ensures that the work performance of the whole charging gateway is not influenced obviously.