Niels Peek,Pedro Pereira Rodrigues
Niels Peek
The routine operation of modern healthcare systems produces a wealth of data in electronic health records, administrative databases, clinical registries, and other clinical systems. It is widely acknowledged that there is great potential fo...
Data science as a language: challenges for computer science-a position paper [0.03%]
数据科学作为一种语言：计算机科学面临的挑战——观点论文
Arno Siebes
Arno Siebes
In this paper, I posit that from a research point of view, Data Science is a language. More precisely Data Science is doing Science using computer science as a language for datafied sciences; much as mathematics is the language of, e.g., ph...
Michail Tsagris,Giorgos Borboudakis,Vincenzo Lagani et al.
Michail Tsagris et al.
We address the problem of constraint-based causal discovery with mixed data types, such as (but not limited to) continuous, binary, multinomial, and ordinal variables. We use likelihood-ratio tests based on appropriate regression models and...
Ivo D Dinov
Ivo D Dinov
Data Science is a bridge discipline connecting fundamental science, applied disciplines, and the arts. The demand for novel data science methods is well established. However, there is much less agreement on the core aspects of representatio...
Comparison of strategies for scalable causal discovery of latent variable models from mixed data [0.03%]
混合数据隐变量模型的可扩展因果发现方法比较
Vineet K Raghu,Joseph D Ramsey,Alison Morris et al.
Vineet K Raghu et al.
Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data ("Mixed Data"), and essential variables may be unobserved in this data d...
Bryan Andrews,Joseph Ramsey,Gregory F Cooper
Bryan Andrews
In this paper we outline two novel scoring methods for learning Bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated Bayesian network...
Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD [0.03%]
处理混合和缺失数据的基于约束的因果发现以研究注意力缺陷多动障碍的原因学
Elena Sokolova,Daniel von Rhein,Jilly Naaijen et al.
Elena Sokolova et al.
Causal discovery is an increasingly important method for data analysis in the field of medical research. In this paper, we consider two challenges in causal discovery that occur very often when working with medical data: a mixture of discre...
A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images [0.03%]
一百万个变量及以上：在高维图模型中用于学习图形因果关系的快速贪婪搜索算法，及其在功能性磁共振成像中的应用
Joseph Ramsey,Madelyn Glymour,Ruben Sanchez-Romero et al.
Joseph Ramsey et al.
We describe two modifications that parallelize and reorganize caching in the well-known Greedy Equivalence Search (GES) algorithm for discovering directed acyclic graphs on random variables from sample values. We apply one of these modifica...