UC Irvine

Population-based models are prevalent in different aspects of our lives. Decision-making in clinical health relies heavily upon the insights generated from population-based models such as recommended blood pressure and blood glucose values. These models and insights are easy to understand, but they do not necessarily capture the variance at an individual level. These are also likely to suffer from bias due to the participants' selection strategy (selection bias) or statistical limitations of the models used. Therefore, population-based models are not precise enough to make predictions at an individual level and highlight the need for personalized models for individual decision-making. N-of-1 analysis and modeling strategies are gaining popularity for addressing this problem. This modeling paradigm views every individual as a unique system and reduces variance at an individual level. Multimodal data generated by individuals and systems can be utilized to enable such analysis. Different wearable and IoT devices and smartphone applications capture data about individual lifestyles and health as daily life events and associated data streams. These event streams and data streams create a lifelog for the user that captures their habits and behaviors in the form of frequent event patterns.

In this work, we propose an interactive event analysis system that leverages the multimodal events and data streams from varied sources and enables analysts to perform N-of-1 analysis for individuals. The system is based on an event patterns language to represent temporal event relationships and extends the language to allow the creation of aggregate features from event patterns. The proposed system has three major components that allow the analysts to perform N-of-1 analysis:1. Event creation modules define new complex events from multimodal data using pre-defined event combination operators and motif discovery from data streams. Different event visualizations enable the analysts to explore the event space and identify event combinations for further analysis. 2. A hypothesis testing module allows the analyst to encode domain knowledge and personal beliefs in the form of a directed acyclic graph where every edge in the graph captures a causal relationship between the parameters (represented by the nodes). These relationships can then be tested using an implementation of the do-operator and estimate the effect of an intervention on the observed outcome. 3. A data-driven frequent event sequence detection module allows the analysts to discover frequent sequences of events and the time delays between the events leading up to an outcome event of interest. These frequent sequences describe the commonly observed events associated with the outcome and may represent a causal link between the events, which may be tested empirically or experimentally.