Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
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DOI：
10.1186/s12911-020-1023-5
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发表时间：
2020-02-03
影响因子：
3.5
通讯作者：
Jurman, Giuseppe
中科院分区：
文献类型：
--
作者：
Chicco, Davide;Jurman, Giuseppe
研究背景心血管疾病每年在全球造成约1700万人死亡，主要表现为心肌梗死和心力衰竭。当心脏无法泵出足够的血液来满足身体的需要时，就会发生心力衰竭（HF）。可用的患者电子病历量化了症状、身体特征和临床实验室检查值，可用于进行生物统计学分析，旨在突出医生无法检测到的模式和相关性。特别是，机器学习可以从数据中预测患者的生存情况，并可以在其医疗记录中列出最重要的特征。方法分析2015年收集的299例心力衰竭患者的数据集。我们应用了几种机器学习分类器来预测患者的生存率，并对与最重要的风险因素相对应的特征进行排名。我们还通过采用传统的生物统计学测试来执行替代特征排名分析，并将这些结果与机器学习算法提供的结果进行比较。由于这两种特征排名方法都清楚地将血清肌酐和射血分数确定为两个最相关的特征，因此我们仅基于这两个因素构建机器学习生存预测模型。我们对这两个特征模型的研究结果表明，不仅血清肌酐和射血分数足以从医疗记录中预测心力衰竭患者的生存率，而且单独使用这两个特征可以比使用原始数据集的全部特征进行更准确的预测。我们还进行了一项分析，包括每个患者的随访月份：即使在这种情况下，血清肌酐和射血分数也是数据集最具预测性的临床特征，足以预测患者的生存率。结论这一发现有可能影响临床实践，成为医生预测心力衰竭患者是否存活的新支持工具。事实上，旨在了解患者在心力衰竭后是否会存活的医生可能主要关注血清肌酐和射血分数。
Background Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records. Methods In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone. Results Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival. Conclusions This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.
影响因子：
4.6
作者：
Sakamoto M;Fukuda H;Kim J;Ide T;Kinugawa S;Fukushima A;Tsutsui H;Ishii A;Ito S;Asanuma H;Asakura M;Washio T;Kitakaze M
通讯作者：
Kitakaze M