All Times EDT
Keywords: machine learning, data mining, clinical development, real-world evidence, patient journey
Patient journey is a collection of topics including understanding patient experience in the healthcare ecosystem in order to improve patient care, as well as understanding the disease development paths and their relation to clinical outcomes to potentially facilitate clinical decision making. Electronic health records (EHR) and health insurance administrative databases have provided rich and inexpensive sources of information for patient journey research. We have looked into temporal disease pattern identification in such databases for a rare disease to improve disease understanding, and we have applied sequential pattern mining and network analysis in the exploratory phase. Many results are in alignment with clinical understanding of the disease and we hope it can also facilitate hypothesis generation for further research. We will discuss methods and limitations and share experience for analyzing such large-scale health databases.