Abstract: Artificial intelligence-based systems and methods for learning management are described.

Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

Abstract: In one example embodiment of the invention, a simulation based training system is provided having a sensor that unobtrusively collects objective data for individuals and teams experiencing training content to determine the cognitive states of individuals and teams; time-synchronizes the various data streams; automatically determines granular and objective measures for individual cognitive load (CL) of individuals and teams; and automatically determines a cognitive load balance (CLB) and a relative cognitive load (RCL) measure in real or near-real time. Data is unobtrusively gathered through physiological or other activity sensors such as electroencephalogram (EEG) and electrocardiogram (ECG) sensors. Some embodiments are further configured to also include sociometric data in the determining cognitive load. Sociometric data may be obtained through the use of sociometric badges.

Abstract: Systems and methods for supporting medical therapy decisions are disclosed that utilize predictive models and electronic medical records (EMR) data to provide predictions of health conditions over varying time horizons. Embodiments also determine a 0-100 health risk index value that represents the “risk” for a patient to acquire a health condition based on a combination of real-time and predicted EMR data. The systems and methods receive EMR data and use the predictive models to predict one or more data values from the EMR data as diagnostic criteria. In some embodiments, the health condition trying to be avoided is Sepsis and the health risk index is a Sepsis Risk Index (SRI). In some embodiments, the predictive models are neural network models such as time delay neural networks.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: A multifunctional neural network system for prediction which includes memory components to store previous values of data within a network. The memory components provide the system with the ability to learn relationships/patterns existent in the data over time.

Abstract: A predictive technique for treating diabetes mellitus is described whereby a patient's blood glucose levels are monitored “continuously” over an extended period of time and a life-event diary is maintained records all significant life-events (e.g., food intake, medication, exercise, mood/emotions, etc.). This information is analyzed to derive a mathematical model that closely matches the patient's glucose level variations for the period of monitoring. Specific daily time periods of dysglycemic vulnerability are determined by calculating when the mathematical model predicts that crossings of predetermined hyperglycemic and hypoglycemic threshold levels will occur. These predicted periods of vulnerability are then used to devise a therapeutic plan that administers treatment in anticipation of predicted dysglycemic excursions, thereby limiting the extent of those excursions or eliminating them altogether.