Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), 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.

Abstract: 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.

Abstract: System (10) for extracting a fetal heart rate from at least one maternal signal using a computer processor (26). The system includes sensors (12-18) attached to a patient to receive abdominal ECG signals and a recorder and digitizer (20) to record and digitize each at least one maternal signal in a maternal signal buffer (22A-22D). The system further includes a peak detector (40) to identify candidate peaks in the maternal signal buffer. The signal stacker (42) 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 (44) 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.

Abstract: A system (300) configured to analyze electronic medical records comprises: a user interface (310) configured to receive input from a user and to receive a request for patient information; and a processor (320) comprising: a patient cohort generator (350) configured to: (i) track user input; (ii) identify patient information accessed through the user interface as well as patient parameters associated with the patient; (iii) associate patients into a patient cohort based on the patient parameters; (iv) identify, for the patient cohort, types of information most commonly accessed by the users; and (v) associate the identified types of information with the patient cohort; and a record identifier (370) configured to: (i) associate the patient for whom patient information is requested with a patient cohort; and (ii) identify, based on the patient cohort with whom the patient is associated, the types of information associated with that cohort.

Abstract: The present disclosure is directed to methods and apparatus for centralized de-identification of protected data associated with subjects. In various embodiments, de-identified data may be received (1102) that includes de-identified data set(s) associated with subject(s) that is generated from raw data set(s) associated with the subjects. Each of the raw data set(s) may include identifying feature(s) that are usable to identify the respective subject. At least some of the identifying feature(s) may be absent from or obfuscated in the de-identified data. Labels associated with each of the de-identified data sets may be determined (1104). At least some of the de-identified data sets may be applied (1108) as input across a trained machine learning model to generate respective outputs, which may be compared (1110) to the labels to determine a measure of vulnerability of the de-identified data to re-identification.

Abstract: The present disclosure is directed to centralized de-identification of protected data associated with subjects in multiple modalities based on a hierarchal taxonomy of policies and handlers. In various embodiments, data set(s) associated with subject(s) may be received. Each of the data set(s) may contain data points associated with a respective subject. The data points associated with the respective subject may include multiple data types, at least some of which are usable to identify the respective subject. For each respective subject: a classification of each of the data points may be determined in accordance with a hierarchal taxonomy; based on the classifications, respective handlers for the data points may be identified; and each data point of the plurality of data points may be processed using a respective identified handler, thereby de-identifying the plurality of data points associated with the respective subject.

Abstract: 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.

Abstract: System (10) for extracting a fetal heart rate from at least one maternal signal using a computer processor (26). The system includes sensors (12-18) attached to a patient to receive abdominal ECG signals and a recorder and digitizer (20) to record and digitize each at least one maternal signal in a maternal signal buffer (22A-22D). The system further includes a peak detector (40) to identify candidate peaks in the maternal signal buffer. The signal stacker (42) 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 (44) 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.

Abstract: System (10) for extracting a fetal heart rate from at least one maternal signal using a computer processor (26). The system includes sensors (12-18) attached to a patient to receive abdominal ECG signals and a recorder and digitizer (20) to record and digitize each at least one maternal signal in a maternal signal buffer (22A-22D). The system further includes a peak detector (40) to identify candidate peaks in the maternal signal buffer. The signal stacker (42) 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 (44) 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.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), 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.

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: The present disclosure pertains to obtaining information that facilitates determining healthcare quality measures by evaluating subject healthcare data in real-time. Information is obtained that facilitates determination of compliance with healthcare quality measures. This is accomplished by running queries on a clinical database comprising subject healthcare data. Natural language processing is utilized to extract subject healthcare data at various times from the clinical database based on individual queries, thus determining any changes in subject healthcare data over time. A rule-based component is used to implement healthcare quality measures and evaluate updated subject healthcare data based upon rules.

Abstract: The present disclosure relates to estimation and use of clinician assessment of patient acuity. In various embodiments, a plurality of patient feature vectors associated with a plurality of respective patients may be obtained (302, 304). Each patient feature vector may include one or more health indicator features indicative of observable health indicators of a patient, and one or more treatment features indicative of characteristics of treatment provided to the patient. A machine learning model (216) may be trained (306) based on the patient feature vectors to receive, as input, subsequent patient feature vectors, and to provide, as output, indications of levels of clinician acuity assessment. Later, a patient feature vector associated with a given patient may be provided (404) as input to the machine learning model. Based on output from the machine learning model, a level of clinician acuity assessment associated with the given patient may be estimated (406) and used (408-416) for various applications.

Abstract: Various embodiments described herein relate to methods and apparatuses for documenting data by tracking user interactions with an interface. Users such as medical personnel or the like rely on clinical documentation to treat a patient. By automatically generating clinical documentation based on user interactions with an interface when reviewing patient data, users are not required to spend time in generating clinical documentation themselves.

Abstract: Techniques are described herein for collapsing clinical event data into meaningful states of patient care. In various embodiments, time-ordered streams of clinical data associated with a plurality of respective patients may be divided into one or more respective pluralities of temporal segments. Each stream of clinical data may indicate a clinical history of a particular patient of the plurality of patients. Each of the one or more pluralities of temporal segments may have a different duration. In some embodiments, embedding(s) of the one or more pluralities of temporal segments into reduced dimensionality space(s) may be generated. Process mining may be performed on the embedding(s). Based on the process mining, one or more temporal health trajectories shared among the plurality of patients may be identified.

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: System (10) for extracting a fetal heart rate from at least one maternal signal using a computer processor (26). The system includes sensors (12-18)attached to a patient to receive abdominal ECG signals and a recorder and digitizer (20)to record and digitize each at least one maternal signal in a maternal signal buffer (22A-22D). The system further includes a peak detector (40) to identify candidate peaks in the maternal signal buffer. The signal stacker (42)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 (44)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.

Abstract: A piston assembly, which may be part of a concentric slave cylinder for use in an automatic transmission, is provided. The piston assembly includes a piston and a piston cylinder disposed adjacent to the piston and having portions forming a receiving slot. A piston guide is attached to the piston. The piston guide has a main body portion and an alignment protrusion extending from the main body portion. The alignment protrusion is disposed in the receiving slot of the piston cylinder. The alignment protrusion includes a main alignment portion and a spring lever. The spring lever is separated from the main alignment portion by a gap. Compressible material is disposed in the gap.