Temporal Network Visual Analytics to Track Symptom Evolution During Disease Progression
There is a substantial challenge to diagnosing many diseases in an early state, as symptoms may rarely emerge as a solitary disease process. Co-morbidities may mimic or mask the presence of a disease, and potentially can lead to false-positive or negative diagnoses. In practice, physicians often make difficult diagnoses in the moment using their clinical knowledge, and not necessarily based on a quantitative assessment of longitudinal patient data from electronic health records (EHRs). This is mainly due to the lack of available, analytical tools to help such decision makers extract meaningful patterns and insights from EHRs in a timely manner.
To address such challenges, we built MatrixFlow, a visual analytic tool designed to help aid medical decision makers and researchers by making the subtle trends of disease progression more obvious. The goal of our work is that by unearthing the hidden patterns in patient health records, emerging health risks may become more discoverable and earlier diagnoses of diseases can occur so clinicians and patients can proactively develop preventative strategies to reduce negative future outcomes.
Adam Perer, Jimeng Sun. MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression. American Medical Informatics Association Annual Symposium (AMIA 2012). Chicago, Illinois. (2012).