This study explores machine learning methods for the detection of unexpected findings in Spanish radiology reports. Regarding radiological reports, unexpected findings are the set of radiological signs identified at a certain imaging modality exam which meet two characteristics: they are not apparently related with the a priori expected results of the radiological exam and involve a clinical emergency or urgency situation that must be reported shortly to the prescribing physician or another medical specialist as well as to the patient in order to preserve life and/or prevent dangerous occurrences. Several traditional machine learning and deep learning classification algorithms are evaluated and compared. To carry out the task we use 5947 anonymous radiology reports from HT médica. Experimental results suggest that the performance of the Convolutional Neural Networks models are better than traditional machine learning. The best F1 score for the identification of an unexpected finding was 90%. Finally, we also perform an error analysis which will guide us to achieve better results in the future.

这项研究探索了用于检测西班牙放射学报告中意外发现的机器学习方法。关于放射学报告，意外发现是在某种影像学检查中确定的符合以下两个特征的放射学征象集：它们显然与放射学检查的先验预期结果不相关，并且涉及必须紧急应对的临床紧急情况或紧急情况为了保护生命和/或防止发生危险事故，不久后要向开处方的医生或其他医学专家以及患者报告。对几种传统的机器学习和深度学习分类算法进行了评估和比较。为了执行此任务，我们使用了HTmédica的5947个匿名放射学报告。实验结果表明，卷积神经网络模型的性能优于传统的机器学习。识别意外发现的最佳F1分数是90％。最后，我们还将执行错误分析，这将指导我们将来取得更好的结果。