Predicting Outcome in Critically Ill Patients using Artificial Intelligence Models

机译：使用人工智能模型预测重症患者的结果

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Outcomes research in the Intensive Care Units (ICUs) is gaining momentum supported and motivated on recent social, medical and technological developments. A lot of models for mortality prediction have been proposed and adopted in the ICUs (e.g., APACHE, SAPS); however none of these models takes into account the intermediate outcomes (incidence and duration of Out-of-Range Measurements of the monitored parameters). The existence of several databases containing clinical data collected from ICUs enabled the application of Data Mining techniques like the Artificial Neural Networks (ANNs) in a Knowledge Discovery from Databases (KDD) process to induce predictive models in a more flexible and efficient fashion than the classical approaches as the Logistic Regression. This paper argues in this direction presenting an experimental and comparative study on the use of ANNs in "outcome prediction" analysing the impact of intermediate outcomes (physiological impairment). The overall KDD process is dissected and some preliminary results are presented and discussed.

机译：重症监护病房（ICUs）的成果研究在近期的社会，医学和技术发展中得到了支持和激励。在ICU中已经提出并采用了许多死亡率预测模型（例如，APACHE，SAPS）;但是，这些模型都没有考虑中间结果（监视参数的范围外测量的发生率和持续时间）。包含从ICU收集到的临床数据的几个数据库的存在，使诸如人工神经网络（ANN）的数据挖掘技术在数据库知识发现（KDD）过程中得到了应用，从而比传统方法更灵活，更有效地引入了预测模型接近逻辑回归。本文朝这个方向提出了实验性和比较性研究，即在“结果预测”中使用人工神经网络来分析中间结果（生理损伤）的影响。剖析了整个KDD过程，并提出和讨论了一些初步结果。

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《Artificial Intelligence and Applications》|2002年|p.47-51|共5页
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Manuel Santos; Alvaro Silva; Antonio Abelha; Lopes Gomes;
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中图分类自动化技术、计算机技术;
关键词
critically Ill patients; mortality predicting models based on intermediate outcomes; knowledge discovery from databases; data mining; artificial neural networks;

机译：重病患者;基于中间结果的死亡率预测模型;从数据库中发现知识;数据挖掘;人工神经网络;

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2. Prognostic models based on administrative data alone inadequately predict the survival outcomes for critically ill patients at 180 days post-hospital discharge [J] . Journal of critical care . 2012,第4期

机译：仅基于行政数据的预后模型不足以预测重症患者住院后180天的生存结果
3. Validity of four models for predicting outcome in critically ill acute renal failure patients. [J] . Halstenberg WK, Goormastic M, Paganini EP Clinical nephrology . 1997,第2期

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4. Predicting Outcome in Critically Ill Patients using Artificial Intelligence Models [C] . Manuel Santos, Alvaro Silva, Antonio Abelha, IASTED international conference on artificial intelligence and applications . 2002

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6. Predicting outcomes in critically ill patients in a resource-poor setting: the Rwanda Mortality Probability Model [O] . T Twagirumugabe, E Riviello, R Fowler, 2015

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Predicting Outcome in Critically Ill Patients using Artificial Intelligence Models

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