论文标题
从手术室的医疗程序中信任机器学习结果
Trusting Machine Learning Results from Medical Procedures in the Operating Room
论文作者
论文摘要
机器学习可用于分析生理数据。检测脑缺血是一项对患者护理产生很大影响的成就。我们试图研究非侵入性监测器的持续生理数据，并且对机器学习的分析可以在不同的环境中检测出脑缺血，在手术中进行颈动脉内膜切除术和在急性中风中血管内血栓切除术期间。我们在详细介绍了两个不同组的结果和一名患者的结果。尽管CEA患者的结果是一致的，但血栓切除术患者的患者却没有，并且经常包含极端值，例如准确性1.0。我们认为这是由于过程的短期和质量不佳的数据的持续时间的结果，导致数据集较小。因此，这些结果不可信。
Machine learning can be used to analyse physiological data for several purposes. Detection of cerebral ischemia is an achievement that would have high impact on patient care. We attempted to study if collection of continous physiological data from non-invasive monitors, and analysis with machine learning could detect cerebral ischemia in tho different setting, during surgery for carotid endarterectomy and during endovascular thrombectomy in acute stroke. We compare the results from the two different group and one patient from each group in details. While results from CEA-patients are consistent, those from thrombectomy patients are not and frequently contain extreme values such as 1.0 in accuracy. We conlcude that this is a result of short duration of the procedure and abundance of data with bad quality resulting in small data sets. These results can therefore not be trusted.
