Li Cindy, Chen Elizabeth, Savova Guergana, Fraser Hamish, Eickhoff Carsten
Center for Biomedical Informatics, Brown University, Providence, RI, United States.
Computational Health Informatics Program, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:360-366. eCollection 2020.
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.
诊断错误会对患者安全构成严重威胁，导致严重伤害甚至死亡。目前正在努力开发干预措施，使医生能够重新评估错误并提高诊断准确性。我们的研究对从PubMed摘要中挖掘出的误诊模式进行了探索。选择了包含某些表明误诊的短语的文章标题，并计算了这些误诊的频率。我们以有向图的形式呈现结果模式，其中频率加权的误诊边连接诊断顶点。我们发现，最常被误诊的疾病往往被误诊为多种不同的疾病，每种误诊的频率相对较低，而不是更有可能被误诊为单一疾病。此外，虽然误诊关系通常可能存在，但往往发现这种关系是单向的。
