Trustworthy Explanations for Knowledge Discovered from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. Embedded in the big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Many of these techniques apply "opaque box" approaches to make accurate predictions. However, these techniques may not be crystal clear to the users. As the users not necessarily be able to clearly view the entire knowledge discovery (e.g., prediction) process, they may not easily trust the discovered knowledge (e.g., predictions). Hence, in this paper, we present a system for providing trustworthy explanations for knowledge discovered from e-health records. Specifically, our system provides users with global explanations for the important features among the records. It also provides users with local explanations for a particular record. Evaluation results on real-life e-health records show the practicality of our system in providing trustworthy explanations to knowledge discovered (e.g., accurate predictions made).