Graph Kernel Prediction of Drug Prescription

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

doi:10.1109/bhi.2019.8834676

Cited by 6 publications

(14 citation statements)

References 30 publications

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“…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.…”

Section: Preliminariesmentioning

confidence: 99%

“…We point readers to these articles [16] for a more in-depth graph kernel discussion and [4] for a better understanding and design principle of graph kernel and its associated feature maps. In [5,15], several graph kernels were proposed to solve the drug prescription outcome prediction problem as patient graph classification. Please refer [5,15] for more in-depth descriptions on kernel definitions.…”

Section: Patient Graphmentioning

confidence: 99%

“…In [5,15], several graph kernels were proposed to solve the drug prescription outcome prediction problem as patient graph classification. Please refer [5,15] for more in-depth descriptions on kernel definitions.…”

Section: Patient Graphmentioning

confidence: 99%

“…They can further reduce medication errors and identify possible drug prescription pathways to pursue for clinical personnel. 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].…”

Section: Introductionmentioning

confidence: 99%

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

2021

J. Nonlinear Var. Anal.

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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.

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Section: Preliminariesmentioning

confidence: 99%

Section: Patient Graphmentioning

confidence: 99%

Section: Patient Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

J. Nonlinear Var. Anal.

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“…As patient medical data is typical time series data, almost all time series data analysis methods can be deployed for medical event prediction. They may fall into the following three categories: (1) statistic analysis methods such as Cox proportional hazards model [12] and hierarchical Association Rule Model (HARM) [13]; (2) statistic machine learning methods based on manually-crafted features such as artificial neural network, decision tree, logistic regression, Support Vector Machines (SVM) [14,15]; (3) deep learning methods such as auto-encode model [16], CNN [11], RNN [17], LSTM and Bi-LSTM [18]. For example, in the early studies, McCormick et al's proposed a Hierarchical Association Rule Model (HARM) to predict disease risk from medical data using association analysis and Bayesian estimation [13].…”

Section: Medical Event Predictionmentioning

confidence: 99%

A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

Liu

Ding³

et al. 2020

Int. J. Mach. Learn. & Cyber.

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Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.

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Self-Supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

Yao,

Cao,

Russell

et al. 2024

ACM Trans. Comput. Healthcare

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Learning Electronic Health Records (EHRs) representation is a preeminent yet under-discovered research topic. It benefits various clinical decision support applications, e.g., medication outcome prediction or patient similarity search. Current approaches focus on task-specific label supervision on vectorized sequential EHR, which is not applicable to large-scale unsupervised scenarios. Recently, contrastive learning has shown great success in self-supervised representation learning problems. However, complex temporality often degrades the performance. We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems. Unlike the state-of-the-art, we do not change the graph structure to construct augmented views. Instead, we use Kernel Subspace Augmentation to embed nodes into two geometrically different manifold views. The entire framework is trained by contrasting nodes and graph representations on those two manifold views through the commonly used contrastive objectives. Empirically, using publicly available benchmark EHR datasets, our approach yields performance on clinical downstream tasks that exceeds the state-of-the-art. Theoretically, the variation in distance metrics naturally creates different views as data augmentation without changing graph structures. Practically, our method is non-ad hoc and confirms superior performance on commonly used graph benchmark datasets.

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Graph Kernel Prediction of Drug Prescription

Cited by 6 publications

References 30 publications

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

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

A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

Self-Supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

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