Departamento de Engenharia Electrotécnica e de Computadores, Instituto Superior Técnico, ULisboa, Portugal; Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal.
Departamento de Engenharia Electrotécnica e de Computadores, Instituto Superior Técnico, ULisboa, Portugal; Instituto de Telecomunicações, Av. Rovisco Pais, 1049-001, Lisboa, Portugal.
Comput Methods Programs Biomed. 2018 Aug;162:11-18. doi: 10.1016/j.cmpb.2018.05.002. Epub 2018 May 3.
Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection.
We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood.
Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses.
In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community.
药代动力学研究药物在体内随时间的吸收、分布、代谢和排泄。临床药代动力学专注于治疗管理，通过研究药物治疗的疗效和毒性，为个性化医学提供了重要的见解。当以相同的药物剂量开始治疗时，患者药物血浓度的高度变异性会阻碍该研究。最近，人们已经提出了对药代动力学反应进行聚类的方法，以分层患者，并为每个层次提供不同的药物剂量。然而，这种聚类方法不能使用用户定义的参数自动确定正确的聚类数量，该参数用于合并距离给定启发式阈值更近的聚类。我们旨在使用信息论方法解决无参数模型选择。
我们提出了两种基于最小描述长度和归一化最大似然的药代动力学反应聚类的模型选择标准。
实验结果表明，模型选择方案能够揭示混合药代动力学反应背后的正确聚类数量。
在这项工作中，我们能够设计两种模型选择标准来确定药代动力学曲线混合物中的聚类数量，这比以前的工作有所改进。我们还提供了一种基于 Java 的、高效的并行实现，以供社区使用。
