Abstract: Various embodiments of the present disclosure provide systems and methods for prediction of a risk for mild or severe persistent post-operative pain (POP) for an individual of interest. A risk prediction may be determined based at least in part on a cohort predictive model. The cohort predictive model is associated with a surgical type cohort and initialized with historical multivariate intra-operative vital sign data associated with binary classifications of mild or severe persistent post-operative pain. Using complex higher-order singular value decomposition, phase information for the historical multivariate intra-operative vital sign data is determined. A relationship between phase information and mild or severe persistent POP is then determined using discriminant analysis. Subsequently, phase information for multivariate intra operative vital sign data for an individual of interest is provided to a cohort predictive model, which uses the determined relationship to classify the individual of interest.

Abstract: A patient monitoring system comprises a plurality of sensors and an analysis computing entity. The sensors comprise a visual sensor and a wearable sensor. The visual sensor is configured to capture images of a patient and provide the images of the patient such that the analysis computing entity receives the images. The wearable sensor is configured to capture wearable data. The wearable data comprises (a) biometric data of the patient and/or (b) movement data of the patient. The wearable sensor is configured to provide the wearable data such that the analysis computing entity receives the wearable data. The analysis computing entity is configured to receive the images of the patient and the wearable data, analyze at least one of the images of the patient and the wearable data to determine objective patient data, and update a patient record based on the objective patient data.

Abstract: Methods, apparatus, systems, and computer program products for providing patient predictions are provided in various embodiments. Responsive to receiving an indication of initiation of a patient interaction, a model for the patient is initiated by an assessment computing entity. The model has been trained using machine learning and the model is configured to generate a prediction for the patient. The prediction comprises at least one of an acuity score or a mortality prediction. Responsive to identifying a prediction trigger, the assessment computing entity updates the model for the patient based at least in part on medical data corresponding to the patient. The assessment computing entity generates the prediction using the updated deep learning model. The assessment computing entity provides at least a portion of the prediction such that the at least a portion of the prediction may be used to update an electronic health record corresponding to the patient and/or provided to a clinician for review.

Abstract: Methods and systems disclosed herein utilize an automated analytics framework to implement a perioperative complication risk algorithm that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for eight major postoperative complications. An example method includes accessing health record data for a patient, normalizing the accessed health record data to generate a health record data set for the patient, transforming one or more features from the health record data set, selecting one or more transformed features from the health record data set, calculating risk probabilities for one or more complication risk categories based on the health record data set and the selected one or more features, calculating mortality risk probabilities for one or more mortality risk categories based on the calculated risk probabilities, and generating a personalized risk panel based on the calculated risk probabilities and the calculated mortality risk probabilities.

Abstract: A patient monitoring system comprises a plurality of sensors and an analysis computing entity. The sensors comprise a visual sensor and a wearable sensor. The visual sensor is configured to capture images of a patient and provide the images of the patient such that the analysis computing entity receives the images. The wearable sensor is configured to capture wearable data. The wearable data comprises (a) biometric data of the patient and/or (b) movement data of the patient. The wearable sensor is configured to provide the wearable data such that the analysis computing entity receives the wearable data. The analysis computing entity is configured to receive the images of the patient and the wearable data, analyze at least one of the images of the patient and the wearable data to determine objective patient data, and update a patient record based on the objective patient data.

Abstract: Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.

Abstract: Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.

Abstract: Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.

Abstract: Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.

Abstract: Several embodiments of systems and methods for adaptive smart environment automation are described herein. In one embodiment, a computer implemented method includes determining a plurality of sequence patterns of data points in a set of input data corresponding to a plurality of sensors in a space. The input data include a plurality of data points corresponding to each of the sensors, and the sequence patterns are at least partially discontinuous. The method also includes generating a plurality of statistical models based on the plurality of sequence patterns, and the individual statistical models corresponding to an activity of a user. The method further includes recognizing the activity of the user based on the statistical models and additional input data from the sensors.