Ontology driven temporal event annotator mHealth application framework

Amente Bekele, Joe Samuel, Shermeen Nizami, Amna Basharat, Randy Giffen, James R. Green

Abstract: We present an application (app) framework to facilitate the collection of gold standard temporal event annotations. These data will enable training and evaluation of machine learning algorithms for predicting events of clinical significance. Recording of such data using pen and paper can prove to be tedious and error-prone due to the variation in the types of events and the frequency of occurrence. To address this problem, we developed an mHealth application framework that presents an intuitive and configurable user interface for annotating a timeline with events. The presented Temporal Event Annotator (TEA) app framework supports dynamically building a customized application inclusive of events, event categories, and study attributes based on the design input of a specific study. This is accomplished by presenting a terminology schema for the hierarchical definition of event types and an additional user interface (UI) schema to support UI-specific attributes. We describe the framework architecture independent of specific technology implementations. We also describe specific instantiations of the framework that we used to develop and evaluate apps for three different use cases: 1) patient monitoring in the Neonatal Intensive Care Unit (NICU), 2) estimating patient stress levels during immersive rehabilitation therapy, and 3) quantifying the patient experience during emergency neonatal transport. The TEA framework provides a reliable and intuitive solution for temporal event annotation that accounts for the unique experimental requirements of each study.