Multiple Graph Kernel Fusion Prediction of Drug Prescription

Yao, Hao-Ren; Chang, Der–Chen; Huang, Wei; Lee, Tian–Shyug

doi:10.1145/3307339.3342134

Cited by 6 publications

(28 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our previous efforts [5,12,15] present a graph kernel-based system for outcome prediction of drug prescription, particularly the success or failure treatment, on short-term and chronic diseases. In [5], a Multiple Graph Kernel Fusion (MGKF) was proposed to overcome noise effect on short-term disease. A deep graph kernel learning approach, e.g., Cross-Global Attention Graph Kernel Network (Cross-Global), is proposed in [12] to handle long-term chronic disease.…”

Section: Preliminariesmentioning

confidence: 99%

“…For a chronic disease, we consider a multiple medication treatment plan with long-term outcome observation and medical history (e.g., 10 years prior to the diagnostic). We refer readers to MGKF [5] and Cross-Global [12] for greater detail.…”

Section: Preliminariesmentioning

confidence: 99%

“…Distance metrics can also be part of the kernel function design [4]. In [5], the authors used the Euclidean distance as part of a graph kernel to capture time difference similarity between patients. In [6], the authors demonstrated the positive definiteness for a distance substitution kernel.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], a framework was proposed to predict success and failure outcomes of a given drug prescription for antibiotic treatment-based disease. The approach is further extended to overcome biased data distribution [5] and chronic disease treatment plan [12]. Moreover, Euclidean and cosine distance measures exhibit different performance behaviors.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], the cosine distance was found superior to the Euclidean measure under highly data imbalanced chronic disease, however, the Euclidean distance was used for short-term disease in [5]. To further investigate such differences, we establish a unified framework, which integrates two model structures proposed in the aforementioned works, to conduct rigorous empirical evaluation on all diseases investigated in previous studies [5,12], in addition to a theoretical discussion. The aforementioned prescription efficacy prediction approaches are now under commercial licensing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The analysis from nonlinear distance metric to kernel-based prescription prediction system

2021

J. Nonlinear Var. Anal.

View full text Add to dashboard Cite

The distance metric and its nonlinear variant play a substantial role in machine learning, particularly yoso in building kernel functions. Often, the Euclidean distance with a radial basis function (RBF) is used to construct a RBF kernel for nonlinear classification. However, domain implications periodically constrain the distance metrics. Specifically, within the domain of drug efficacy prediction, distance measures must account for time that varies based on disease duration, short to chronic. Recently, a distance-derived graph kernel approach was commercially licensed for drug prescription efficacy prediction. The analysis of the distance functions used therein, namely the Euclidean and cosine distance measures and their respective derived graph kernels, is provided. Theoretically, we provide a formulation of our efforts and demonstrate how both the Euclidean and cosine distance induce space and discuss the difference from geometric perspectives. The aforementioned approach is likewise empirically evaluated using a million-plus patient subset of a life-spanning, real-world, electronic health record database. Diseases are characterized as either short in duration or chronic and either common, hence balanced data, or relatively rare, hence imbalanced. Empirically, the system accurately predicted the efficacy of prescriptions for both balanced and imbalanced and short-term and chronic diseases, with at least one of the measures used being statistically significantly superior to conventional prediction methods. Succinctly, for short-term, balanced diseases, the Euclidean and cosine measures were generally statistically equivalent. For short-term, imbalanced diseases however, the Euclidean measure was superior to the cosine measure, at times and not infrequently, statistically significantly so. For chronic, balanced diseases, Euclidean was slightly superior to the cosine measure, but they were statistically equivalent. In contrast, for chronic, imbalanced diseases, the cosine measure was consistently statistically significantly superior to the Euclidean measure. These findings indicate the need for both measures depending on the use case. Our empirical findings match our theoretical underpinnings.

show abstract

Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The analysis from nonlinear distance metric to kernel-based prescription prediction system

2021

J. Nonlinear Var. Anal.

View full text Add to dashboard Cite

show abstract

Dropout Graph Product for Improved Relationship Discovery Across Multiple Heterogeneous Graphs

2021

View full text Add to dashboard Cite

Relationship discovery across multiple heterogeneous graphs has recently attracted considerable interest. A major challenge is how to fuse and utilize the structure and properties of multiple heterogeneous graphs complementarily to improve relationship discovery between graph pairs where there are only a few observed relationships. In this paper, we seek to solve the problem through label propagation on dropout graph product. We first map multiple heterogeneous graphs onto a single homogeneous graph through graph product. The internal structure and properties of each individual graphs, as well as the observed inter-graph relationships across multiple graphs are fused losslessly via assigning edge weights and encoding node vectors of the graph product. As a result, the complex problem of relationship discovery across multiple heterogeneous graphs is transformed into a simple problem of node classification on homogeneous graph product. However, the size of graph product increases quickly with the size and the number of factor graphs, which leads to low accuracy and high computational cost. Dropout is therefore introduced to address this problem, which is applied to label propagation on graph product. Finally, we combine the results of a set of label propagations on different dropout graph products using a product of experts model. The proposed approach is characterized by flexibility in defining graph product, good generalization ability, high accuracy, and efficient computation. The experiments on real-world datasets show that the proposed approach significantly improves relationship discovery across multiple heterogeneous graphs, obtaining better results on the benchmark datasets than the baseline approaches.

show abstract