使用机器学习的危重病人风险预测和严重程度的实时临床决策系统

Ammanath Gopal, M. Sailatha, S. Vikas, G. Sampath
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引用次数: 1

摘要

本文通过对重症监护病房住院患者的症状进行预测。该系统在患者床边运行,预测病情,及时为患者提供基本治疗。通过向患者提供基本药物,可以预防严重情况和情况的发生。在医院,有一个使用三段式方法的决策系统,这很容易延迟和不准确。该系统通过考虑适度的数据集,消除了不准确和延迟的结果,从而产生更好和更快的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Real Time Clinical Decision System for Risk Prediction and Severity in Critical Ill Patients Using Machine Learning
This paper predicts the diseases of the patients by considering their symptoms who are admitted in the critical care units. This system operates at the bed side of the patients and predicts the diseases so that the basic treatment is provided to the patients without any delay. By providing basic medication to the patients, the occurrence of serious conditions and circumstances can be prevented. In hospitals, there is a decision system that operates using three phase approach which is prone to delay and inaccuracy. The proposed system eradicates the inaccurate and delayed results by considering the moderate datasets and hence yields better and fast results.
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