Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations.
Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations.
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DOI：
10.1056/nejmp1702071
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发表时间：
2017-06-29
期刊：
影响因子：
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通讯作者：
Asch SM
中科院分区：
文献类型：
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作者：
Chen JH;Asch SM
我们都听说过，大数据有望通过广泛捕获电子健康记录和大量数据流来改变医疗保健，这些数据来自从保险索赔和登记到个人基因组学和生物传感器等各种来源。计算机智能和机器学习预测算法已经可以自动驾驶汽车、识别口语和检测信用卡欺诈，它们是解锁数据的关键，可以精确地为实时决策提供信息。但在新兴技术的“炒作周期”中，机器学习现在骑在“膨胀的期望的顶峰”上。2预测对医学来说并不新鲜。从风险评分指导抗凝治疗（CHADS 2）和胆固醇药物的使用（ASCVD）到重症监护病房（APACHE）患者的风险分层，数据驱动的临床预测在医疗实践中是常规的。结合现代机器学习，临床数据源使我们能够快速生成数千个类似临床问题的预测模型。从败血症的早期预警系统到超人的成像诊断，这些方法的潜在适用性是巨大的。
Big data, we have all heard, promise to transform health care with the widespread capture of electronic health records and high-volume data streams from sources ranging from insurance claims and registries to personal genomics and biosensors. 1 Artificial-intelligence and machine-learning predictive algorithms, which can already automatically drive cars, recognize spoken language, and detect credit card fraud, are the keys to unlocking the data that can precisely inform real-time decisions. But in the “hype cycle” of emerging technologies, machine learning now rides atop the “peak of inflated expectations.” 2Prediction is not new to medicine. From risk scores to guide anticoagulation (CHADS2) and the use of cholesterol medications (ASCVD) to risk stratification of patients in the intensive care unit (APACHE), data-driven clinical predictions are routine in medical practice. In combination with modern machine learning, clinical data sources enable us to rapidly generate prediction models for thousands of similar clinical questions. From early-warning systems for sepsis to superhuman imaging diagnostics, the potential applicability of these approaches is substantial.