A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support.
A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support.
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DOI：
10.1136/amiajnl-2011-000751
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发表时间：
2012-06
期刊：
影响因子：
--
通讯作者：
Ohno-Machado L
中科院分区：
文献类型：
--
作者：
Jiang X;Boxwala AA;El-Kareh R;Kim J;Ohno-Machado L
在线提供竞争工具，以评估发展某些感兴趣的疾病（如心血管疾病）的风险。虽然预测模型已经开发并验证了队列研究的数据，但很少注意确保这种预测对个人的可靠性，这对护理决策至关重要。其目标是开发一种患者驱动的自适应预测技术，以改善个性化的风险估计，为临床决策提供支持。提出了一种数据驱动的方法，该方法利用个体化置信区间（CI）从候选人库中选择最“合适”的模型来评估个体患者的临床状况。该方法不需要访问训练数据集。这种方法与其他策略进行了比较：最佳模型（理想模型，只能通过访问数据或了解哪个群体与个体最相似来实现），CROSS模型和随机模型选择。当在临床数据集上进行评估时，该方法在区分度（p<1 e-14）和校准（p<0.006）方面显著优于CROSS模型选择策略。该方法在区分度方面优于随机模型选择策略（p<1 e-12），但该改进未达到校准的显著性（p=0.1375）。CI可能并不总是提供足够的信息来对预测的可靠性进行排名，并且该评估是使用聚合进行的。如果一个特定的个体与现有模型的训练集中所代表的个体非常不同，那么CI可能有点误导。这种方法有可能提供更可靠的预测比其他药物提供的个别患者的疾病风险估计。
Competing tools are available online to assess the risk of developing certain conditions of interest, such as cardiovascular disease. While predictive models have been developed and validated on data from cohort studies, little attention has been paid to ensure the reliability of such predictions for individuals, which is critical for care decisions. The goal was to develop a patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support. A data-driven approach was proposed that utilizes individualized confidence intervals (CIs) to select the most ‘appropriate’ model from a pool of candidates to assess the individual patient's clinical condition. The method does not require access to the training dataset. This approach was compared with other strategies: the BEST model (the ideal model, which can only be achieved by access to data or knowledge of which population is most similar to the individual), CROSS model, and RANDOM model selection. When evaluated on clinical datasets, the approach significantly outperformed the CROSS model selection strategy in terms of discrimination (p<1e–14) and calibration (p<0.006). The method outperformed the RANDOM model selection strategy in terms of discrimination (p<1e–12), but the improvement did not achieve significance for calibration (p=0.1375). The CI may not always offer enough information to rank the reliability of predictions, and this evaluation was done using aggregation. If a particular individual is very different from those represented in a training set of existing models, the CI may be somewhat misleading. This approach has the potential to offer more reliable predictions than those offered by other heuristics for disease risk estimation of individual patients.
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DOI：
10.1136/amiajnl-2011-000291
发表时间：
2012-03
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Jiang X;Osl M;Kim J;Ohno-Machado L
通讯作者：
Ohno-Machado L
DOI：
10.1136/amiajnl-2010-000004
发表时间：
2012-03-01
影响因子：
6.4
作者：
Lasserre, Julia;Arnold, Steffen;Hinrichs, Carl
通讯作者：
Hinrichs, Carl
影响因子：
5
作者：
Karp, I;Abrahamowicz, M;Pilote, L
通讯作者：
Pilote, L
DOI：
10.1136/amiajnl-2011-000394
发表时间：
2012-05
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Frisse ME;Johnson KB;Nian H;Davison CL;Gadd CS;Unertl KM;Turri PA;Chen Q
通讯作者：
Chen Q
DOI：
10.1136/amiajnl-2011-000360
发表时间：
2012-01-01
影响因子：
6.4
作者：
Eppenga, Willemijn L.;Derijks, Hieronymus J.;De Smet, Peter A. G. M.
通讯作者：
De Smet, Peter A. G. M.