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.