Predicting dire outcomes of patients with community acquired pneumonia
Predicting dire outcomes of patients with community acquired pneumonia
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DOI：
10.1016/j.jbi.2005.02.005
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发表时间：
2005-10-01
影响因子：
4.5
通讯作者：
Spirtes, P
中科院分区：
文献类型：
--
作者：
Cooper, GF;Abraham, V;Spirtes, P
社区获得性肺炎（CAP）是一种与患者死亡率、患者发病率和医疗资源利用有关的重要临床疾病。对CAP患者可能的临床过程的评估可以显著影响关于是否将患者作为住院患者或门诊患者治疗的决策。这一决定反过来又会影响资源利用率以及患者的健康。预测可怕的结果，如死亡率或严重的临床并发症，是评估患者临床过程中特别重要的组成部分。我们使用了1601例CAP患者病例的训练集，构建了11个预测可怕结果的统计和机器学习模型。我们评估了686个额外的CAP患者病例的模型。主要目标不是将这些学习算法作为研究终点进行比较;相反，它是为了开发最好的模型来预测可怕的结果。一个特殊版本的人工神经网络（NN）模型预测可怕的结果是最好的。使用686个测试案例，我们估计了在实践中应用NN模型的预期医疗质量和成本影响。这一分析的具体定量结果是基于我们明确提出的一些假设;它们需要进一步研究和验证。尽管如此，分析的一般含义似乎是稳健的，即即使是对流行和昂贵疾病（如CAP）的预测性能的微小改善，也可能导致医疗保健服务质量和效率的显着改善。因此，寻找具有最高预测性能的模型是很重要的。因此，寻求更好的机器学习和统计建模方法具有重要的现实意义。(c)2005年爱思唯尔公司All rights reserved.
Comm unity-acquired pneumonia (CAP) is an important clinical condition with regard to patient mortality, patient morbidity, and healthcare resource utilization. The assessment of the likely clinical course of a CAP patient can significantly influence decision making about whether to treat the patient as an inpatient or as an outpatient. That decision can in turn influence resource utilization, as well as patient well being. Predicting dire outcomes, such as mortality or severe clinical complications, is a particularly important component in assessing the clinical course of patients. We used a training set of 1601 CAP patient cases to construct 11 statistical and machine-learning models that predict dire outcomes. We evaluated the resulting models on 686 additional CAP-patient cases. The primary goal was not to compare these learning algorithms as a study end point; rather, it was to develop the best model possible to predict dire outcomes. A special version of an artificial neural network (NN) model predicted dire outcomes the best. Using the 686 test cases, we estimated the expected healthcare quality and cost impact of applying the NN model in practice. The particular, quantitative results of this analysis are based on a number of assumptions that we make explicit; they will require further study and validation. Nonetheless, the general implication of the analysis seems robust, namely, that even small improvements in predictive performance for prevalent and costly diseases, such as CAP, are likely to result in significant improvements in the quality and efficiency of healthcare delivery. Therefore, seeking models with the highest possible level of predictive performance is important. Consequently, seeking ever better machine-learning and statistical modeling methods is of great practical significance. (c) 2005 Elsevier Inc. All rights reserved.