Koninklijke Philips Corporation N.V.

Eric Thomas Carlson

Minnan Xu

Srinivasan Vairavan

Limei Cheng

Larry James Eshelman

Erina Ghosh

Bo Liu

Lin Yang

David Paul Noren

Mohammad Shahed Sorower

A method () for training a scoring system () comprising the steps of: (i) providing () a scoring system comprising a scoring module (); (ii) receiving () a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Method and system for automated inclusion or exclusion criteria detection

Techniques disclosed herein relate to removing potentially identifying features of a specific subject from a data set to prevent re-identification of the subject using an external data source. In various embodiments, the data set contains, as potential identifying features of the specific subject, multiple bursts of temporally-proximate events. Time blocks within the data set can be identified to capture one or more of the bursts of temporally-proximate events for the specific subject. Adding random time shifts for each time block can add noise to the data set and remove or obfuscate the identifying features of a specific subject to generate a time shifted data set.

Time blocking noising for de-identification

System () for extracting a fetal heart rate from at least one maternal signal using a computer processor (). The system includes sensors (-) attached to a patient to receive abdominal ECG signals and a recorder and digitizer () to record and digitize each at least one maternal signal in a maternal signal buffer (A-D). The system further includes a peak detector () to identify candidate peaks in the maternal signal buffer. The signal stacker () of the system stacks the divides at least one maternal signal buffer into a plurality of snippets, each snippet including one candidate peak and a spatial filter () to identify and attenuate a maternal QRS signal in the plurality of snippets of the maternal signal buffer, the spatial filter including at least one of principal component analysis and orthogonal projection, to produce a raw fetal ECG signal which is stored in a raw fetal ECG buffer. The system further includes a fetal QRS identifier () for identifying peaks in the raw fetal ECG buffer by at least one of principal component analysis and a peak-detector followed by rule based fQRS extraction and a merger () to calculate and merge the fetal heart rate from the identified peaks.

Fetal heart rate extraction from maternal abdominal ECG recordings

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. If there is a chance that the actual score is higher and may exceed a critical alarm threshold, a notification can be issued to advise a new measurement reading to improve score confidence.

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New York, NY, United States of America

Eric Thomas Carlson