Abstract
This exploratory study aims to design a system that allows physicians to take advantage of the available data and data sources to manage inflammatory bowel disease (IBD). This study will: (1) Explore the sources of data relevant to IBD management including EMR, wearable devices, and mobile personal health records. (2) Design a system that interfaces with these data sources to capture and store data in an IBD data warehouse. (3) Develop the algorithms necessary for data cleansing and for applying descriptive and predictive analytics to provide physicians with relevant data to predict future IBD flares. (4) Design a system interface that is easy to use and that can be integrated into physicians’ workflows. This study will inform the development of a system that enhances patients’ quality of life and reduce cost associated with patients’ hospitalization and medication.
Recommended Citation
Abouzahra, Mohamed, "Designing a System to Predict Inflammatory Bowel Disease Flares Using Machine Learning" (2019). AMCIS 2019 Proceedings. 7.
https://aisel.aisnet.org/amcis2019/healthcare_it/healthcare_it/7
Designing a System to Predict Inflammatory Bowel Disease Flares Using Machine Learning
This exploratory study aims to design a system that allows physicians to take advantage of the available data and data sources to manage inflammatory bowel disease (IBD). This study will: (1) Explore the sources of data relevant to IBD management including EMR, wearable devices, and mobile personal health records. (2) Design a system that interfaces with these data sources to capture and store data in an IBD data warehouse. (3) Develop the algorithms necessary for data cleansing and for applying descriptive and predictive analytics to provide physicians with relevant data to predict future IBD flares. (4) Design a system interface that is easy to use and that can be integrated into physicians’ workflows. This study will inform the development of a system that enhances patients’ quality of life and reduce cost associated with patients’ hospitalization and medication.