利用机器学习提高手术效果

Sindhura Bonthu, P. Armijo, Tiffany Tanner, Qiuming A. Zhu
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引用次数: 0

摘要

预测患者病情的严重程度有助于提供准确的临床护理。由于患者数据的不同特征,死亡率预测是一个挑战。患者数据高度稀疏、高度偏倚和不平衡、高度混杂,对其进行评估是一个具有挑战性的问题。在本文中,我们专注于使用神经网络处理大量数据,这些数据可以进一步用于分析以获得有用的见解,例如识别导致某些事件结果的主要特征,或根据某些属性及其测量值对不同对象进行分类。
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Using Machine Learning to Improve Surgical Outcomes
Predicting the severity of patient’s condition helps providing accurate clinical care. Mortality prediction is one of the challenges due to distinct characteristics of the patient’s data. It is a challenging problem to evaluate the patient’s data which is highly sparse, highly biased and imbalanced, and highly mixed. In this paper, we are focusing on processing large volumes of data using neural networks which can be further used for analysis to obtain useful insights, such as identifying the major features contributing to certain outcomes of events or classifying different objects based on the presences of certain attributes and their measurements.
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