University of California

Myung-Kyung Suh

Majid Sarrafzadeh

Mars Lan

Hassan Ghasemzadeh

Nabil Alshurafa

Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the system's effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. Embodiments of the present disclosure may utilize machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall.

Systems and methods for missing data imputation

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

Myung-Kyung Suh