An ontology driven framework for the analysis and reporting of oncology combination therapy prescription

Accurate real-time market intelligence helps product providers track their market penetration vs. competitors and quickly identify trends in order to increase overall marketing effectiveness and maximize revenue. The resulting faster analysis allows clients to react more quickly to market events, and to better assess and tune their marketing activities. This dissertation is concerned with the integration of ontologies with EDC (Electronic Data Capture) software for the purpose of managing drugs used for oncology treatment modalities. Four critical challenges were identified: (1) Linking the individual drugs and their associated synonyms to the correct oncology treatment combinations; (2) Designing an Ontology integrated with market definitions; (3) Interfacing with the ontology used to manage the combination therapies; and (4) Automatically generating SQL artifacts based on an ontology for querying a relational database. In order to address these challenges, it is proposed the following developments: (a) Oncology Treatment Combination with Market Definitions ontology that captures and collects the oncology treatment combinations organized by user specified rule base market definitions, (b) a Product Library User Module interface designed to interface with the ontology used to manage the combination therapies and (c) a Treatment Combination Identification Service that automatically generates SQL artifacts for querying the data warehouse.;The position taken in this dissertation is that the identification, management, and querying of oncology treatment modalities can be more effectively handled with ontological engineering than in a purely relational database approach. The result of an ontological approach has shown an improvement in the accuracy of reporting which oncology treatment combinations was prescribed.;For this study a new Ontology was designed and a new search process was implemented to increase inferencing efficiency and reporting accuracy. It was the premise of this dissertation that representing the domain of knowledge for a therapeutic area using an ontology, as opposed to a purely relational data model, would significantly improve the inferencing capability within a community of practice and the implementation time for software development. The contributions of this dissertation are the following: (i) OTCMD ontology, which is the first to integrate oncology treatment combinations with market definitions into a single ontology; (ii) A unique user interfaces that manages oncology treatment combinations in the ontology; (iii) TCIS, which is one of the first data access services that consumes the ontology and automatically generates and executes the necessary SQL statements for a request; (iv) Usability study that investigates the quality, accuracy and efficiency of semantic and non-semantic data retrieval and reporting on oncology treatment combinations; and (v) Propose a method for resolving inconsistencies between different data sources.