  Diagnostic errors can pose a serious threat to patient safety, leading to
serious harm and even death. Efforts are being made to develop interventions
that allow physicians to reassess for errors and improve diagnostic accuracy.
Our study presents an exploration of misdiagnosis patterns mined from PubMed
abstracts. Article titles containing certain phrases indicating misdiagnosis
were selected and frequencies of these misdiagnoses calculated. We present the
resulting patterns in the form of a directed graph with frequency-weighted
misdiagnosis edges connecting diagnosis vertices. We find that the most
commonly misdiagnosed diseases were often misdiagnosed as many different
diseases, with each misdiagnosis having a relatively low frequency, rather than
as a single disease with greater probability. Additionally, while a
misdiagnosis relationship may generally exist, the relationship was often found
to be one-sided.
