Mining Misdiagnosis Patterns from Biomedical Literature
This addresses diagnostic errors in healthcare to improve patient safety, but it is incremental as it applies existing text-mining methods to biomedical literature.
The study mined misdiagnosis patterns from PubMed abstracts to identify common diagnostic errors, finding that the most misdiagnosed diseases were often confused with many different diseases at low frequencies rather than a single high-probability alternative, and misdiagnosis relationships were often one-sided.
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.