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首页> 外文会议>IEEE International Conference on Bioinformatics Biomedicine >Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods
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Intracranial pressure level prediction in traumatic brain injury by extracting features from multiple sources and using machine learning methods

机译：通过从多个来源提取特征和使用机器学习方法来提取创伤性脑损伤的颅内压力水平预测

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摘要

This paper proposes a non-intrusive method to predict/estimate the intracranial pressure (ICP) level based on features extracted from multiple sources. Specifically, these features include midline shift measurement and texture features extracted from CT slices, as well as patient's demographic information, such as age. Injury Severity Score is also considered. After aggregating features from slices, a feature selection scheme is applied to select the most informative features. Support vector machine (SVM) is used to train the data and build the prediction model. The validation is performed with 10 fold cross validation. To avoid overfitting, all the feature selection and parameter selection are done using training data during the 10 fold cross validation for evaluation. This results an nested cross validation scheme implemented using Rapidminer. The final classification result shows the effectiveness of the proposed method in ICP prediction.
机译：本文提出了一种非侵入式方法来预测/估计来自多种来源提取的特征的颅内压（ICP）水平。具体而言，这些功能包括中线移位测量和从CT切片中提取的纹理特征，以及患者的人口统计信息，例如年龄。还考虑了伤害严重程度分数。在从切片聚合特征后，应用特征选择方案来选择最具信息性的功能。支持向量机（SVM）用于培训数据并构建预测模型。验证以10倍交叉验证执行。为避免过度装备，所​​有特征选择和参数选择都是使用培训数据在10倍交叉验证中进行评估。结果结果使用RAPIDMINER实现了嵌套的交叉验证方案。最终的分类结果显示了ICP预测中提出的方法的有效性。

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