We show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent volume and variety of big prescription data help in constructing predictive models for the automatic detection of at-risk profiles.

Our methodology is designed to validate our claims that: (1) drug prescription data on the day of admission contain rich information about the patient's situation and perspectives of evolution, and (2) the various perspectives of big medical data (such as veracity, volume, variety) help in extracting this information. We build binary classification models to identify at-risk patient profiles. We use a distributed architecture to ensure scalability of model construction with large volumes of medical records and clinical data.

We report on practical experiments with real data of millions of patients and hundreds of hospitals. We demonstrate how the fine-grained analysis of such big data can improve the detection of at-risk patients, making it possible to construct more accurate predictive models that significantly benefit from volume and variety, while satisfying important criteria to be deployed in hospitals.

我们展示了对大量药物处方数据的分析如何使在入院当天发现住院期间有发生并发症风险的患者成为可能。我们首次探索了在何种程度上，大量的大处方数据可以帮助构建用于自动检测有风险特征的预测模型。

我们的方法论旨在验证我们的主张：（1）入院当天的药物处方数据包含有关患者情况和演变观点的丰富信息，以及（2）大医学数据的各种观点（例如准确性，数量） （多种）帮助提取此信息。我们建立了二进制分类模型，以识别高危患者档案。我们使用分布式架构来确保具有大量病历和临床数据的模型构建的可伸缩性。

我们报告了具有数百万患者和数百家医院的真实数据的实际实验。我们展示了对这样的大数据进行细粒度分析如何改善对高危患者的检测，使构建更准确的预测模型成为可能，而该模型可从数量和种类中受益匪浅，同时又可满足在医院中部署的重要标准。