Background and objective: Assignment of medical imaging procedure protocols requires extensive knowledge about patient’s data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient’s diagnosis. Artificial intelligence has proven to be of great help in these healthcare-related problems, and specifically the application of Natural Language Processing (NLP) techniques for extracting information from text reports has been successfully used in automatic text classification tasks.

Methods: In this paper, machine learning classification models based on NLP have been developed using patient’s data present in radiological reports and radiological imaging protocols. We have used a real corpus provided by the private medical center “HT medica” composed of almost 700,000 Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) examinations obtained during routine clinical use. We have compared several models including traditional machine learning methods such as support vector machine and random forest, neural networks and transfer language techniques.

Results: The results obtained are encouraging taking into account that the system is performing a complex text multiclass classification task. Specifically, for the best proposed system we obtain 92.2% accuracy in the CT dataset and 86.9% in the MRI dataset.

Conclusions: The best machine learning system is potentially efficient, quality and cost effective. For this reason it is currently used in real scenarios by radiologists as decision support tool for assigning protocols of CT and MRI studies.

背景和目的：分配医学成像程序协议需要广泛了解患者数据，通常包括在放射线申请表和放射线报告中。在进行放射学检查之前，需要分配方案，确定每个患者的程序。该协议分配过程的自动化可以提高患者诊断的效率。事实证明，人工智能对这些与医疗保健相关的问题有很大帮助，特别是自然语言处理（NLP）技术从文本报告中提取信息的应用已成功用于自动文本分类任务中。

方法：本文利用放射报告和放射成像方案中存在的患者数据开发了基于NLP的机器学习分类模型。我们使用了由私人医疗中心“ HT medica”提供的真实主体，该主体由常规临床使用期间获得的将近700,000台计算机断层扫描（CT）和磁共振成像（MRI）检查组成。我们比较了几种模型，包括传统的机器学习方法，例如支持向量机和随机森林，神经网络和传输语言技术。

结果：考虑到系统正在执行复杂的文本多类分类任务，因此获得的结果令人鼓舞。具体来说，对于建议的最佳系统，我们在CT数据集中的准确度为92.2％，在MRI数据集中的准确度为86.9％。

结论：最佳的机器学习系统具有潜在的效率，质量和成本效益。因此，放射线医生目前在实际情况下将其用作决策支持工具，以分配CT和MRI研究方案。