Predicting changes in systolic blood pressure using longitudinal patient records.
Predicting changes in systolic blood pressure using longitudinal patient records.
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DOI：
10.1016/j.jbi.2015.06.024
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发表时间：
2015-12
影响因子：
4.5
通讯作者：
Nielsen RD
中科院分区：
文献类型：
--
作者：
Solomon JW;Nielsen RD
本文介绍了一种基于纵向临床记录的结构化和非结构化（基于文本的）信息预测收缩压（SBP）未来变化的模型。对于每位患者，临床记录按时间顺序排序，并从中提取收缩压测量值。该模型根据之前的临床记录预测未来收缩压的变化。这是通过使用特征选择算法对显著特征进行最小中值二乘回归来实现的。使用预测模型，在未见的测试数据上实现了0.47的相关系数（p < 0.0001）。这与0.39的基线相关系数形成对比。左图横轴表示患者收缩压的实际变化，纵轴表示模型预测的收缩压变化。对角线贯穿图中“实际收缩压变化”等于“预测收缩压变化”的所有点。因此，绘制的点越靠近对角线，预测就越准确。
This paper introduces a model that predicts future changes in systolic blood pressure (SBP) based on structured and unstructured (text-based) information from longitudinal clinical records. For each patient, the clinical records are sorted in chronological order and SBP measurements are extracted from them. The model predicts future changes in SBP based on the preceding clinical notes. This is accomplished using least median squares regression on salient features found using a feature selection algorithm. Using the prediction model, a correlation coefficient of 0.47 is achieved on unseen test data (p < .0001). This is in contrast to a baseline correlation coefficient of 0.39. The graph on the left represents the actual change in a patient’s SBP on the horizontal axis, and the change in SBP predicted by the model on the vertical axis. The diagonal line goes through all points in the graph where “Actual SBP Change” is equal to “Predicted SBP Change”. As such, the closer a plotted point is to the diagonal line, the more accurate the prediction.