Abstract: A system (100) for assessing fluid responsiveness includes an infusion pump (24) in communication with at least one processor (32), and a plurality of physiological monitors (40,42,44,46) operable to receive physiological signals from an associated patient. Physiological signals (48,50) acquired from the associated patient (10) during a fluid challenge are synchronized with a timing signal (54) of the infusion pump (24) administering the fluid challenge. One or more dynamic indices and/or features (58) is calculated from the synchronized physiological signals (50), and one or more dynamic indices and/or features (50) is calculated from baseline physiological signals (48) acquired from the associated patient (10) prior to the fluid challenge. A fluid responsiveness probability value (64) of the patient (10) is determined based on dynamic indices and/or features (58) from the synchronized physiological signals (50) and dynamic indices and/or features (50) from the baseline physiological signals (48).

Abstract: The following relates generally to the medical monitoring arts, medical warning systems concerning a monitored patient, and so forth. In clinical settings, alarms are usually triggered when a single-parameter or a multi-parameter score exceeds certain thresholds. When a score needs to be determined, if certain parameters are not available, the common practice is to use the most recent measurements of the parameters for the score calculation. However, a patient's status may change from moment to moment. The parameters measured hours ago may not be a good indicator of the patient's current status. This uncertainty can put deteriorating patients at great risk. An embodiment uses statistical methods to estimate a range of scores and the probability of these scores if old measurements have to be used for score determination. Instead of giving a single number at a time, a confidence interval may be displayed to emphasize the fact that the score is determined partially based on old measurements.

Abstract: An early warning system for patient monitoring includes one or more patient monitors (620) configured to generate patient physiological data, a patient database (602) storing patient physiological measurements and outcomes, and one or more computer processors (604) programmed to: machine learn an Aggregate Weighted Track and Trigger System (AWTTS) algorithm for quantifying patient condition by an AWTTS score based on a training set of the patient physiological measurements and outcomes; apply an Early Warning Score or Modified Early Warning Score (EWS) algorithm to patient physiological measurements to generate EWS scores; apply the machine-learned AWTTS algorithm to the patient physiological measurements to generate AWTTS scores; and create a mapping between the AWTTS scores and the EWS scores.

Abstract: When monitoring patients in a general ward, clinical decision support risk scores are evaluated to determine whether a patient should be monitored using a spot check method whereby a caregiver periodically checks the patient, a continuous monitoring method whereby the patient is monitored by a monitoring device such as an electrocardiograph, or whether the patient requires transfer to a progressive care unit (PCU) or intensive care unit (ICU). When the number of patient monitors is not sufficient to assign a monitor to all patients for whom a monitor is desired, CDS score thresholds are adjusted to ensure that the neediest patients are assigned monitors.

Abstract: A medical system (10) and method monitor a patient. Patient data for the patient received. The patient data includes vital sign measurements and laboratory results. A vital signs instability index (VIX) regarding a physiological condition of the patient is calculated from the received vital sign measurements. A laboratory instability index (LIX) regarding the physiological condition is calculated from the received laboratory results. The VIX and the LIX are integrated into an indicator of patient deterioration.

Abstract: A medical modeling system and method predict a risk of a physiological condition, such as mortality, for a patient. Measurements of a plurality of predictive variables for the patient are received. The plurality of predictive variables are predictive of the risk of the physiological condition. The risk of the physiological condition is calculated by applying the received measurements to at least one model modeling the risk of the physiological condition using the plurality of predictive variables. The at least one model includes at least one of a hidden Markov model and a logistic regression model. An indication of the risk of the physiological condition is output to a clinician.

Abstract: A medical system (10) and method monitor a patient. Patient data for the patient received. The patient data includes vital sign measurements and laboratory results. A vital signs instability index (VEX) regarding a physiological condition of the patient is calculated from the received vital sign measurements. A laboratory instability index (LIX) regarding the physiological condition is calculated from the received laboratory results. The VEX and the LIX are integrated into an indicator of patient deterioration.

Abstract: A clinical decision support system (16) monitors one or more patients. The system (16) includes one or more processors (84) programmed to receive patient data for the patients. For each patient, one or more monitoring rules are selected from a plurality of monitoring rules based on patient data availability and/or patient context. A determination is made as to whether a patient is deteriorating using the selected monitoring rules for the patient. In response to determining the patient is deteriorating, an alert is generated.

Abstract: A system and method for subscribing TV programs according to the preferences of the user based on the user's past viewing history. The system keeps track of all the programs watched by the user to generate a profile indicative of the frequency of a particular program being watched. Then, a plurality of subscription plans based on the viewer's viewing habit stored in the user profile is offered to the user, so that the user can selectively pay for the programs of his or her interest, instead of paying for the entire programs.