MedTAKMI-CDI: Interactive knowledge discovery for clinical decision intelligence

This paper describes MedTAKMI-CDI, an online analytical processing system that enables the interactive discovery of knowledge for clinical decision intelligence (CDI). CDI supports decision making by providing in-depth analysis of clinical data from multiple sources. We discuss the fundamental challenges we faced and explain how we met those challenges and developed a prototype experimental CDI system that currently handles clinical information for about 7,000 patients at the National Cancer Center in Japan. We elaborate on a three-layer model (attribute-value pairs, ordered sequences of events, and time-stamped sequences of events) for clinical information, which can represent three different levels of abstraction. This flexibility supports a broad range of analysis, from simple demographic analysis to a mission-critical clinical-path pattern analysis. Rather than a collection of rigid relational schema for clinical information, our relational database system employs a metaschema with patient identifier, time stamp, attribute name, and attribute values. This allows us to modify the representation of clinical information without having to reload the data and rewrite the analytic components. We also describe the analytic functions that are used to understand clinical care practice at the hospital, to obtain an overview of the clinical information, to navigate the clinical information by using the layers of abstraction and the ontologies, and to extract the patterns and rules for clinical paths.