Abstract: System and method for detecting cognitive decline in a subject using a classification system for detecting cognitive decline in the subject based on a speech sample. The classification system is trained using speech data corresponding to audio recordings of speech from normal and cognitive decline patients to generate an ensemble classifier comprising a plurality of component classifiers and an ensemble module. Each of the plurality of component classifiers is a machine-learning classifier configured to generate a component output identifying a sample data as corresponding to a normal patient or a cognitive patient. The machine-learning classifier is generated based on a subset of available features. The ensemble module receives component outputs from all of the component classifiers and generates an ensemble output identifying the sample data as corresponding to a normal or cognitive decline patient based on the component outputs.

Abstract: A system and computer-implemented method for detecting return of depression of a patient is provided. The system comprises a wearable device configured to detect movement of the patient and configured to generate actigraphy data corresponding to the movement of the patient and a computing device for retrieving actigraphy data from the device. The system and method obtain training data, including training actigraphy data, over a training period and train an anomaly detector using the training data. The system and method subsequently obtain test data from the patient, extract a plurality of features from the test data, and analyze the extracted data using the trained anomaly detector. A self-report test is used to determine whether an anomaly identified by the anomaly detector indicates that the patient is likely to experience return of depression.

Abstract: System and method for detecting cognitive decline in a subject using a classification system for detecting cognitive decline in the subject based on a speech sample. The classification system is trained using speech data corresponding to audio recordings of speech from normal and cognitive decline patients to generate an ensemble classifier comprising a plurality of component classifiers and an ensemble module. Each of the plurality of component classifiers is a machine-learning classifier configured to generate a component output identifying a sample data as corresponding to a normal patient or a cognitive patient. The machine-learning classifier is generated based on a subset of available features. The ensemble module receives component outputs from all of the component classifiers and generates an ensemble output identifying the sample data as corresponding to a normal or cognitive decline patient based on the component outputs.

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: Disclosed herein are approaches for monitoring a patient in real-time for ARDS development and providing a biomarker-driven ventilation therapy recommendation tool based on the correlation of various therapy patterns and an ARDS biomarker score. When the ARDS biomarker indicates that a patient has a high ARDS risk, the recommendation tool suggests possible therapy routes based on clinical practice. In response to a high score output by the ARDS detection model, the tool outputs a recommendation to initiate a lung protective ventilation strategy (Low Tidal Volume, high Positive End-Expiratory Pressure (PEEP)). A high ARDS score is recognized to be predictive of the appropriateness of such therapy up to several hours before such intervention is typically initiated under current clinical practices.

Abstract: System and method for detecting cognitive decline in a subject using a classification system for detecting cognitive decline in the subject based on a speech sample. The classification system is trained using speech data corresponding to audio recordings of speech from normal and cognitive decline patients to generate an ensemble classifier comprising a plurality of component classifiers and an ensemble module. Each of the plurality of component classifiers is a machine-learning classifier configured to generate a component output identifying a sample data as corresponding to a normal patient or a cognitive patient. The machine-learning classifier is generated based on a subset of available features. The ensemble module receives component outputs from all of the component classifiers and generates an ensemble output identifying the sample data as corresponding to a normal or cognitive decline patient based on the component outputs.

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 process and system for determining a minimal, ‘pruned’ version of the known ARDS model is provided that quantifies the risk of ARDS in terms of physiologic response of the patient, eliminating the more subjective and/or therapeutic features currently used by the conventional ARDS models. This approach provides an accurate tracking of ARDS risk modeled only on the patient's physiological response and observable reactions, and the decision criteria are selected to provide a positive prediction as soon as possible before an onset of ARDS. In addition, the pruning process also allows the ARDS model to be customized for different medical facility sites using selective combinations of risk factors and rules that yield optimized performance. Additionally, predictions may be provided in cases with missing or outdated data by providing estimates of the missing data, and confidence bounds about the predictions based on the variance of the estimates.

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: Disclosed herein are approaches for monitoring a patient in real-time for ARDS development and providing a biomarker-driven ventilation therapy recommendation tool based on the correlation of various therapy patterns and an ARDS biomarker score. When the ARDS biomarker indicates that a patient has a high ARDS risk, the recommendation tool suggests possible therapy routes based on clinical practice. In response to a high score output by the ARDS detection model, the tool outputs a recommendation to initiate a lung protective ventilation strategy (Low Tidal Volume, high Positive End-Expiratory Pressure (PEEP)). A high ARDS score is recognized to be predictive of the appropriateness of such therapy up to several hours before such intervention is typically initiated under current clinical practices.

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: Using a computer communicating with an electronic medical record (EMR) system, an update in a patient EMR is automatically detected of a physiological parameter that is an input to an illness staging or evaluation clinical guideline. Responsive to detecting of the update of the physiological parameter, instructions are executed using the computer to evaluate the illness staging or evaluation clinical guideline using the updated physiological parameter as an input to the illness staging or evaluation clinical guideline to generate a guideline result. The guideline result is plotted as a function of time on a display device.

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 patient is monitored for a medical condition such as acute lung injury (AL1) by operations including: (i) receiving values of a plurality of physiological parameters for the patient; (ii) computing an AL1 indicator value based at least on the received values of the plurality of physiological parameters for the patient; and (iii) displaying a representation of the computed AL1 indicator value on a display (14, 22). The computing operation (ii) may employ various inference algorithms trained on a training set comprising reference patients to distinguish between reference patients having AL1 and reference patients not having AL1, or may employ an aggregation of two or more such inference algorithms. If patients in an ICU are monitored, the display (22) may simultaneously display a diagrammatic representation of each patient including an identification of the patient and a representation of the AL1 indicator value for the patient.