Cases, Clusters, Densities: Modeling the Nonlinear Dynamics of Complex Health Trajectories
Cases, Clusters, Densities: Modeling the Nonlinear Dynamics of Complex Health Trajectories
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DOI：
10.1002/cplx.21728
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发表时间：
2016-09-01
期刊：
影响因子：
2.3
通讯作者：
Griffiths, Frances
中科院分区：
文献类型：
--
作者：
Castellani, Brian;Rajaram, Rajeev;Griffiths, Frances
在卫生信息学时代，纵向数据建模仍然存在问题。问题是方法：卫生数据是高度非线性和动态的，多层次和多维的，由多个主要/次要趋势组成，并且因果复杂，使得曲线拟合，建模和预测变得困难。目前的研究是探索基于案例的密度（CBD）方法建模复杂轨迹的系列研究中的第四项，该方法具有以下优点：它可以(1)将数据库转换为案例集（k维行向量，即包含k个元素的行）；(2)基于(3)一组被称为轨迹的生物社会变量，计算每种情况的轨迹（速度矢量）；(4)构建理论地图来解释这些痕迹；(5)利用矢量量化（即k-means，地形神经网络）将病例轨迹纵向聚类为主要/次要趋势；(6)利用遗传算法和常微分方程创建这些轨迹的微观（矢量场）模型（反问题）；(7)在微观模型中寻找复杂的稳态行为（如螺旋源等）；(8)借鉴热力学、协同学和输运理论，将矢量场（微观模型）转化为宏观密度的线性运动；(9)利用宏观模型模拟已知的和新的基于案例的情景（前向问题）；（10）通过将理论图和k维轮廓与宏观、微观和聚类模型联系起来，构建数据的多个帐户。考虑到这种方法的实用性，我们在这里的目的是组织我们的方法（应用于最近的研究），以便其他人可以使用它。(C) 2015 Wiley期刊公司
In the health informatics era, modeling longitudinal data remains problematic. The issue is method: health data are highly nonlinear and dynamic, multilevel and multidimensional, comprised of multiple major/minor trends, and causally complex-making curve fitting, modeling, and prediction difficult. The current study is fourth in a series exploring a case-based density (CBD) approach for modeling complex trajectories, which has the following advantages: it can (1) convert databases into sets of cases (k dimensional row vectors; i.e., rows containing k elements); (2) compute the trajectory (velocity vector) for each case based on (3) a set of bio-social variables called traces; (4) construct a theoretical map to explain these traces; (5) use vector quantization (i.e., k-means, topographical neural nets) to longitudinally cluster case trajectories into major/minor trends; (6) employ genetic algorithms and ordinary differential equations to create a microscopic (vector field) model (the inverse problem) of these trajectories; (7) look for complex steady-state behaviors (e.g., spiraling sources, etc) in the microscopic model; (8) draw from thermodynamics, synergetics and transport theory to translate the vector field (microscopic model) into the linear movement of macroscopic densities; (9) use the macroscopic model to simulate known and novel case-based scenarios (the forward problem); and (10) construct multiple accounts of the data by linking the theoretical map and k dimensional profile with the macroscopic, microscopic and cluster models. Given the utility of this approach, our purpose here is to organize our method (as applied to recent research) so it can be employed by others. (C) 2015 Wiley Periodicals, Inc.