Discovering hospital admission patterns using models learnt from electronic hospital records
Discovering hospital admission patterns using models learnt from electronic hospital records
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DOI：
10.1093/bioinformatics/btv508
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发表时间：
2015-12-15
期刊：
影响因子：
5.8
通讯作者：
Arandjelovic, Ognjen
中科院分区：
文献类型：
--
作者：
Arandjelovic, Ognjen
动机：电子医疗记录，目前在许多发达国家定期收集，为医学知识的获取开辟了一条新的途径。在这篇文章中，这大量的信息是用来开发一种新的模型入院类型prediction.Results：我介绍了一种新的模型入院类型预测的基础上表示的病人的病史的形式的二进制历史向量。这种表示的动机是使用经验证据从以前的工作和验证使用大型数据语料库的医疗记录从当地医院。所提出的模型允许以直观和易于理解的方式进行探索、可视化和患者特异性预后。它的力量是使用一个大型的，真实世界的数据语料库收集的当地医院，它被证明优于以前的国家的最先进的文献中，实现超过82%的准确性预测的第一个未来的诊断。该模型在长期预后方面也有很大的上级优势，在82%的病例中表现优于以前的工作，而在其余18%的病例中表现相当。
Motivation: Electronic medical records, nowadays routinely collected in many developed countries, open a new avenue for medical knowledge acquisition. In this article, this vast amount of information is used to develop a novel model for hospital admission type prediction.Results: I introduce a novel model for hospital admission-type prediction based on the representation of a patient's medical history in the form of a binary history vector. This representation is motivated using empirical evidence from previous work and validated using a large data corpus of medical records from a local hospital. The proposed model allows exploration, visualization and patient-specific prognosis making in an intuitive and readily understood manner. Its power is demonstrated using a large, real-world data corpus collected by a local hospital on which it is shown to outperform previous state-of-the-art in the literature, achieving over 82% accuracy in the prediction of the first future diagnosis. The model was vastly superior for long-term prognosis as well, outperforming previous work in 82% of the cases, while producing comparable performance in the remaining 18% of the cases.