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之前，从多个医疗机构预测普通患者中的痴呆症，而无需进行任何其他监视或筛查。该模型的目的是对患有痴呆症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自动化。

为此，通过电子病历（EMR）系统广泛使用的常规护理数据被用作数据源。这些数据体现了丰富的知识，并且使相关医疗应用程序易于以经济高效的方式进行大规模部署。具体来说，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。这三个数据集的每一个都用于构建单独的模型以及通过使用所有三个数据集派生的组合模型。选择人类可解释的数据处理和ML技术，以促进多个机构的医疗保健提供者采用建议的模型。

结果表明，该组合模型可在多个机构中推广使用，尽管使用常规护理数据进行了训练，但能够在发病后一年内以接近80％的准确度预测痴呆症。此外，对模型的分析确定了痴呆症的重要预测因子。这些预测因素中的某些（例如，年龄和高血压疾病）已被文献证实，而其他预测因素，尤其是那些来自非结构化医学注释的预测因素，则需要进一步的临床分析。