Time to CARE: a collaborative engine for practical disease prediction
Time to CARE: a collaborative engine for practical disease prediction
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DOI：
10.1007/s10618-009-0156-z
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发表时间：
2010-05-01
影响因子：
4.8
通讯作者：
Barabasi, Albert-Laszlo
中科院分区：
文献类型：
--
作者：
Davis, Darcy A.;Chawla, Nitesh V.;Barabasi, Albert-Laszlo
巨大的医疗费用，特别是慢性病治疗费用，很快就变得难以控制。这场危机促使人们转向预防医学，主要关注的是识别疾病风险并在最早的迹象中采取行动。然而，通用测试既不节省时间也不节省成本。我们提出了CARE，一个协作评估和推荐引擎，它只依赖于患者的病史使用ICD-9-CM代码，以预测未来的疾病风险。CARE使用协同过滤方法，根据每个患者自己的病史和类似患者的病史来预测每个患者的最大疾病风险。我们还描述了一个迭代的版本，ICARE，它结合了合奏概念，以提高性能。此外，我们应用时间敏感的修改，使护理框架实际的长期使用。这些新系统不需要专门的信息，并在一次运行中提供对各种医疗状况的预测。我们在一个大型医疗保险数据集上展示了实验结果，证明CARE和ICARE在捕获未来疾病风险方面表现良好。
The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on patient's medical history using ICD-9-CM codes in order to predict future disease risks. CARE uses collaborative filtering methods to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. Also, we apply time-sensitive modifications which make the CARE framework practical for realistic long-term use. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a large Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.