University of California

Hassan Ghasemzadeh

Majid Sarrafzadeh

Mars Lan

Myung-Kyung Suh

Nabil Alshurafa

Systems, methods, and devices are disclosed that monitor the health status of patients. Dynamic task management functions apply data analytics to discretize continuous data values from monitored patients, and apply association rule mining techniques to prioritized required user tasks, to minimize the number of daily action items required by patients. The remaining action items maximize information gain, thereby improving the overall level of patient adherence and satisfaction without losing health monitoring effectiveness.

Task optimization in remote health monitoring systems

Systems and methods for generalized precursor pattern discovery that work with a wide range of biomedical signals and applications to detect a wide range of medical events are disclosed. In some embodiments, the methods and systems do not require domain-specific knowledge or significant reconfiguration based on the medical event being analyzed, hence it is also possible to discover patterns previously unknown to experts. In some embodiments, to build precursor pattern detection models, the system obtains annotated monitoring data. Positive and negative segments are extracted from the annotated monitoring data, and are preprocessed. Features are extracted from the preprocessed segments, and selected features are chosen from the extracted features. The selected features are classified to create the precursor pattern detection model The precursor pattern detection model may then be used in real time to detect occurrences of the medical event of interest.

Methods and systems for calculating and using statistical models to predict medical events

A method includes receiving contextual data related to at least one of environmental, physiological, behavioral, and historical context, and receiving outcome data related to at least one outcome. The method further includes creating a feature set from the contextual data, selecting a subset of features from the feature set, assigning a score to each feature in the subset of features according to the probability that the feature is a predictor of the at least one outcome, and generating a characteristic curve for the at least one outcome from the subset of features, the characteristic curve being based on the scoring. The method further includes calculating the area under the characteristic curve, and using, the area under the characteristic curve, identifying whether the subset of features is a suitable predictor for the at least one outcome.

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Los Angeles, CA, United States of America

Hassan Ghasemzadeh