Predicting dementia with routine care EMR data
Predicting dementia with routine care EMR data
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DOI：
10.1016/j.artmed.2019.101771
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发表时间：
2020-01-01
影响因子：
7.5
通讯作者：
Boustani, Malaz A.
中科院分区：
文献类型：
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作者：
Ben Miled, Zina;Haas, Kyle;Boustani, Malaz A.
我们的目标是开发一种机器学习(ML)模型，该模型可以在疾病发生前一年和三年从多个医疗保健机构预测普通患者群体中的痴呆，而无需任何额外的监测或筛查。该模型的目的是对有痴呆风险的患者进行具有成本效益的、非侵入性的数字化预筛查。为此，使用通过电子病历(EMR)系统广泛获得的常规护理数据作为数据源。这些数据体现了丰富的知识，使相关的医疗应用程序易于以经济高效的方式大规模部署。具体地说，该模型是通过使用来自三个EMR数据集的结构化和非结构化数据进行训练的：诊断、处方和医疗记录。这三个数据集中的每一个都被用来构建单独的模型以及通过使用所有三个数据集得出的组合模型。为了便于多个机构的医疗服务提供者采用该模型，选择了人类可解释的数据处理和ML技术。结果表明，该组合模型具有跨多个机构的通用性，即使使用常规护理数据进行训练，也能够在发病一年内预测痴呆，准确率接近80%。此外，对模型的分析确定了痴呆症的重要预测因素。其中一些预测因素(例如，年龄和高血压疾病)已经得到文献的证实，而其他因素，特别是来自非结构化医疗记录的预测因素，还需要进一步的临床分析。
Our aim is to develop a machine learning (ML) model that can predict dementia in a general patient population from multiple health care institutions one year and three years prior to the onset of the disease without any additional monitoring or screening. The purpose of the model is to automate the cost-effective, non-invasive, digital pre-screening of patients at risk for dementia.Towards this purpose, routine care data, which is widely available through Electronic Medical Record (EMR) systems is used as a data source. These data embody a rich knowledge and make related medical applications easy to deploy at scale in a cost-effective manner. Specifically, the model is trained by using structured and unstructured data from three EMR data sets: diagnosis, prescriptions, and medical notes. Each of these three data sets is used to construct an individual model along with a combined model which is derived by using all three data sets. Human-interpretable data processing and ML techniques are selected in order to facilitate adoption of the proposed model by health care providers from multiple institutions.The results show that the combined model is generalizable across multiple institutions and is able to predict dementia within one year of its onset with an accuracy of nearly 80% despite the fact that it was trained using routine care data. Moreover, the analysis of the models identified important predictors for dementia. Some of these predictors (e.g., age and hypertensive disorders) are already confirmed by the literature while others, especially the ones derived from the unstructured medical notes, require further clinical analysis.