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首页> 外文会议>International Conference on Ambient Intelligence for Health >Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients
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Real-Time Decision Support Using Data Mining to Predict Blood Pressure Critical Events in Intensive Medicine Patients

机译：使用数据挖掘来预测血压患者血压关键事件的实时决策支持

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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：患者血压是医生的重要力量，做出决定并更好地了解患者状况。在重症监护单位中，可能监测由于患者通过床头监测器连续监测和使用传感器的事实而监测血压。当它们查看监视器或查阅小时收集的值时，强度只能访问生命的符号值。最重要的是收集的值的序列，即，一组最高或最低值可以表示关键的事件，并将未来的并发症带给患者的低血压或高血压。这种并发症可以利用一系列危险的疾病和副作用。这项工作的主要目的是预测患者在下个小时内具有血压临界事件的概率，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。由于输出，模型表示患者的概率（％）在下个小时内具有血压批判性事件。所达到的结果表明非常有前景，呈现敏感性约为95％。

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