Cerner Innovation, Inc.

William Stadler

Douglas S McNair

Kanakasabha K Kailasam

John Christopher Murrish

Sasanka Are

Pedro Alves

Methods, systems, and computer-readable media are provided for managing health status of persons with a chronic condition including providing dynamic, adaptive monitoring, detection, and prediction of suicide risk to a person at risk for suicide related to mental health. In an embodiment, a patient-assessment application is used to obtain information periodically on a patient's mental health status. Based on this information, a logistic regression model is employed to determine a patient's probability of attempting suicide. The probability is evaluated against a default threshold to determine if the patient's status has changed significantly, and if the threshold is exceeded, an action is evoked. In one embodiment, the action includes providing notice to the patient's care provider, caregiver, or case manager.

Decision support for managing mental health conditions

Systems and methods are provided for quantitatively and objectively characterizing sleep architecture in normal individuals and persons with various health conditions. Embodiments of the invention facilitate characterizing temporal-pattern information of an individual's sleep, such as measured by electroencephalography (EEG), for identifying persons with abnormalities in the temporal-pattern information, sequences or durations of their stages of sleeping ('sleep architecture'), for facilitating selecting appropriate therapy or treatment, and for monitoring the effectiveness of such therapy or treatment. In one aspect, a set of time series are formed by electronically representing and storing information pertaining to brain activity, such as EEG hypnogram or sleep information, over a multi-night span. Information for the timeseries is analyzed, using one or more models, such as nonlinear, self-excited threshold autoregressive (SETAR) or neural network models, for determining a measure of chaotic properties of the timeseries. The largest Lyapunov exponent (LLE) is determined for the time series. Statistical departures of a particular patient's LLE values from one or more reference ranges are determined.

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Kansas City, MO, United States of America

William Stadler