A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

中文翻译：

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使用机器学习分类器预测血液透析过程中的低血压出现

患有晚期慢性肾脏疾病的患者在不同的日期接受几次透析。在任何这些疗程的不同小时内，都会监测几个临床参数。这些参数以及其他具有分析性质的参数提供的信息，对于确定患者在治疗过程中可能发生低血压的可能性非常有用，应特别注意，因为它代表了可能的死亡率的可靠因素。但是，分析信息并非始终可供医护人员使用，或者时间太远，因此在治疗过程中监控的临床参数成为预防低血压的关键。本文提出了一项研究，以预测透析期间出现低血压的情况，使用从大型透析数据库训练而来的预测模型，该模型包含对应于758例患者的98,015个疗程的临床信息。该预测模型考虑到了在治疗过程中五次测量的多达22个临床参数以及患者的性别和年龄。该模型通过机器学习分类器进行了训练，预测成功率超过80％。