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Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients

机译：使用数据挖掘进行实时决策支持，以预测重症医学患者的血压关键事件

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摘要

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95 %.
机译：病人的血压是医生作出决定并更好地了解病人状况的重要生命信号。在重症监护病房中，由于患者可以通过床旁监护仪和使用传感器进行连续监护，因此可以监控血压。加强医生仅在看监护仪或每小时查询一次所收集的生命值时，才可以获取生命体征值。最重要的是收集的值的顺序，即一组最高或最低值可以表示严重事件，并给患者带来未来并发症，例如低血压或高血压。这种并发症可以利用一系列危险疾病和副作用。这项工作的主要目标是通过组合实时收集的一组患者数据并使用数据挖掘分类技术，预测下几个小时患者发生血压严重事件的可能性。作为输出，模型表明患者在接下来的一个小时内发生血压严重事件的概率（％）。获得的结果显示出非常有希望的结果，灵敏度约为95％。

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