Department of Electrical and Computer Engineering, School of Engineering and Technology, Indiana University Purdue University at Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202, USA; Regenstrief Institute, Inc., 1101 W. 10th Street, Indianapolis, IN 46202, USA.
Department of Electrical and Computer Engineering, School of Engineering and Technology, Indiana University Purdue University at Indianapolis, 723 W. Michigan Street, Indianapolis, IN 46202, USA.
Artif Intell Med. 2020 Jan;102:101771. doi: 10.1016/j.artmed.2019.101771. Epub 2019 Dec 5.
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.
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作前一年和三年从多个医疗机构预测普通患者人群中的痴呆症，而无需进行任何额外的监测或筛查。该模型的目的是实现针对痴呆症风险患者的经济高效、非侵入性、数字化预筛选的自动化。为此，我们使用电子病历 (EMR) 系统中广泛可用的常规护理数据作为数据源。这些数据蕴含着丰富的知识，使相关的医疗应用能够以经济高效的方式大规模轻松部署。具体来说，该模型是通过使用来自三个 EMR 数据集（诊断、处方和医疗记录）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用于构建一个单独的模型，以及一个使用所有三个数据集构建的组合模型。选择可解释的数据处理和机器学习技术，以便促进来自多个机构的医疗保健提供者采用所提出的模型。结果表明，尽管该模型是使用常规护理数据训练的，但组合模型具有跨多个机构的通用性，并且能够在疾病发作前一年预测痴呆症，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（例如年龄和高血压障碍）已被文献证实，而其他预测因素，尤其是从非结构化医疗记录中得出的预测因素，则需要进一步的临床分析。
