Goodwin Travis, Harabagiu Sanda M
University of Texas at Dallas, Richardson, TX, USA.
AMIA Jt Summits Transl Sci Proc. 2016 Jul 19;2016:78-87. eCollection 2016.
The wealth of clinical information provided by the advent of electronic health records offers an exciting opportunity to improve the quality of patient care. Of particular importance are the risk factors, which indicate possible diagnoses, and the medications which treat them. By analysing which risk factors and medications were mentioned at different times in patients' EHRs, we are able to construct a patient's clinical chronology. This chronology enables us to not only predict how new patient's risk factors may progress, but also to discover patterns of interactions between risk factors and medications. We present a novel probabilistic model of patients' clinical chronologies and demonstrate how this model can be used to (1) predict the way a new patient's risk factors may evolve over time, (2) identify patients with irregular chronologies, and (3) discovering the interactions between pairs of risk factors, and between risk factors and medications over time. Moreover, the model proposed in this paper does not rely on (nor specify) any prior knowledge about any interactions between the risk factors and medications it represents. Thus, our model can be easily applied to any arbitrary set of risk factors and medications derived from a new dataset.
电子健康记录的出现所提供的丰富临床信息为提高患者护理质量带来了令人兴奋的机遇。特别重要的是风险因素，它们表明了可能的诊断，以及用于治疗这些诊断的药物。通过分析患者电子健康记录中不同时间提到的哪些风险因素和药物，我们能够构建患者的临床时间线。这条时间线不仅使我们能够预测新患者的风险因素可能如何发展，还能发现风险因素与药物之间的相互作用模式。我们提出了一种新颖的患者临床时间线概率模型，并展示了该模型如何用于：（1）预测新患者的风险因素随时间的演变方式；（2）识别时间线不规则的患者；（3）发现成对风险因素之间以及风险因素与药物随时间的相互作用。此外，本文提出 的模型不依赖（也未指定）关于其表示的风险因素和药物之间任何相互作用的任何先验知识。因此，我们的模型可以轻松应用于从新数据集中导出的任何任意风险因素和药物集合。
