A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

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Relation Structure-Aware Heterogeneous Information Network Embedding

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33014456 ◽

2019 ◽

Vol 33 ◽

pp. 4456-4463 ◽

Cited By ~ 8

Author(s):

Yuanfu Lu ◽

Chuan Shi ◽

Linmei Hu ◽

Zhiyuan Liu

Keyword(s):

Real World ◽

Dimensional Space ◽

Structural Characteristics ◽

Information Network ◽

Network Embedding ◽

Heterogeneous Information Network ◽

Heterogeneous Information ◽

Real World Datasets ◽

Low Dimensional ◽

Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

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Leveraging Existing Tools in Electronic Health Record Systems to Automate Clinical Registry Compilation

Otolaryngology ◽

10.1177/0194599820901713 ◽

2020 ◽

Vol 162 (3) ◽

pp. 408-409

Author(s):

Alisha Williams ◽

William Goedicke ◽

Kristin A. Tissera ◽

Leila A. Mankarious

Keyword(s):

Hearing Loss ◽

Health Record ◽

Data Input ◽

Software Suite ◽

Clinical Registry ◽

Health Records ◽

Clinical Registries ◽

Time Burden ◽

Research Questions ◽

Medical Health

Clinical registries have proven beneficial by providing a resource to address research questions, monitor care, and identify suitable subjects for clinical studies. Despite a well-organized registry, population is often low because of the human capital required. The increasing prevalence of electronic medical health records provides the opportunity to integrate registry compilation into routine patient encounters. Here we describe how one tool existing within the Epic Medical Record software suite, Smart Phrases, can be adapted to automate population of a hearing loss patient registry. The usage rate of Smart Phrases was high and resulted in a significant reduction in the time burden associated with registry population. Use of Smart Phrases could become an important factor in the design of future registries that allow broad uptake and convenient data input.

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Hydra: a method for strain-minimizing hyperbolic embedding of network- and distance-based data

Journal of Complex Networks ◽

10.1093/comnet/cnaa002 ◽

2020 ◽

Vol 8 (1) ◽

Cited By ~ 2

Author(s):

Martin Keller-Ressel ◽

Stephanie Nargang

Keyword(s):

Computation Time ◽

Feature Space ◽

Hyperbolic Distance ◽

Network Data ◽

Data Points ◽

Low Dimensional ◽

Hyperbolic Embedding ◽

Embedding Methods ◽

Real Network Data

Abstract We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: it minimizes the ‘hyperbolic strain’ between original and embedded data points. Moreover, it is able to recover points exactly, when they are contained in a low-dimensional hyperbolic subspace of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, typically outperforms existing methods in both computation time and embedding quality.

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Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

Applied Sciences ◽

10.3390/app10228003 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8003

Author(s):

Yi-Chun Chen ◽

Cheng-Te Li

Keyword(s):

Social Networks ◽

Large Scale ◽

Main Idea ◽

Feature Representation ◽

Specific Point ◽

Point Of Interest ◽

The Future ◽

Location Based Social Networks ◽

Low Dimensional ◽

Embedding Methods

In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.

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Embedding Hierarchical Structures for Venue Category Representation

ACM Transactions on Information Systems ◽

10.1145/3478285 ◽

2022 ◽

Vol 40 (3) ◽

pp. 1-29

Author(s):

Meng Chen ◽

Lei Zhu ◽

Ronghui Xu ◽

Yang Liu ◽

Xiaohui Yu ◽

...

Keyword(s):

Hierarchical Structure ◽

Hierarchical Structures ◽

Category Representation ◽

Semantic Relationships ◽

The Hierarchical Structure ◽

Sequential Information ◽

Location Based Social Networks ◽

Low Dimensional ◽

The Given ◽

Embedding Methods

Venue categories used in location-based social networks often exhibit a hierarchical structure, together with the category sequences derived from users’ check-ins. The two data modalities provide a wealth of information for us to capture the semantic relationships between those categories. To understand the venue semantics, existing methods usually embed venue categories into low-dimensional spaces by modeling the linear context (i.e., the positional neighbors of the given category) in check-in sequences. However, the hierarchical structure of venue categories, which inherently encodes the relationships between categories, is largely untapped. In this article, we propose a venue C ategory E mbedding M odel named Hier-CEM , which generates a latent representation for each venue category by embedding the Hier archical structure of categories and utilizing multiple types of context. Specifically, we investigate two kinds of hierarchical context based on any given venue category hierarchy and show how to model them together with the linear context collaboratively. We apply Hier-CEM to three tasks on two real check-in datasets collected from Foursquare. Experimental results show that Hier-CEM is better at capturing both semantic and sequential information inherent in venues than state-of-the-art embedding methods.

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Patient satisfaction with electronic medical/health record: a systematic review

Scandinavian Journal of Caring Sciences ◽

10.1111/scs.12015 ◽

2012 ◽

Vol 27 (4) ◽

pp. 785-791 ◽

Cited By ~ 25

Author(s):

Jialin Liu ◽

Li Luo ◽

Riu Zhang ◽

Tingting Huang

Keyword(s):

Systematic Review ◽

Patient Satisfaction ◽

Health Record ◽

Medical Health

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On Investigating Both Effectiveness and Efficiency of Embedding Methods in Task of Similarity Computation of Nodes in Graphs

Applied Sciences ◽

10.3390/app11010162 ◽

2020 ◽

Vol 11 (1) ◽

pp. 162

Author(s):

Masoud Reyhani Hamedani ◽

Sang-Wook Kim

Keyword(s):

Social Relations ◽

Dimensional Space ◽

Similarity Measures ◽

Parameter Tuning ◽

Original Graph ◽

Wide Range ◽

Similarity Computation ◽

Effectiveness And Efficiency ◽

Low Dimensional ◽

Embedding Methods

One of the important tasks in a graph is to compute the similarity between two nodes; link-based similarity measures (in short, similarity measures) are well-known and conventional techniques for this task that exploit the relations between nodes (i.e., links) in the graph. Graph embedding methods (in short, embedding methods) convert nodes in a graph into vectors in a low-dimensional space by preserving social relations among nodes in the original graph. Instead of applying a similarity measure to the graph to compute the similarity between nodes a and b, we can consider the proximity between corresponding vectors of a and b obtained by an embedding method as the similarity between a and b. Although embedding methods have been analyzed in a wide range of machine learning tasks such as link prediction and node classification, they are not investigated in terms of similarity computation of nodes. In this paper, we investigate both effectiveness and efficiency of embedding methods in the task of similarity computation of nodes by comparing them with those of similarity measures. To the best of our knowledge, this is the first work that examines the application of embedding methods in this special task. Based on the results of our extensive experiments with five well-known and publicly available datasets, we found the following observations for embedding methods: (1) with all datasets, they show less effectiveness than similarity measures except for one dataset, (2) they underperform similarity measures with all datasets in terms of efficiency except for one dataset, (3) they have more parameters than similarity measures, thereby leading to a time-consuming parameter tuning process, (4) increasing the number of dimensions does not necessarily improve their effectiveness in computing the similarity of nodes.

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Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding

Applied Sciences ◽

10.3390/app11052371 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2371

Author(s):

Junjian Zhan ◽

Feng Li ◽

Yang Wang ◽

Daoyu Lin ◽

Guangluan Xu

Keyword(s):

State Of The Art ◽

Global Information ◽

Network Embedding ◽

Sampling Process ◽

Attributed Network ◽

Benchmark Datasets ◽

Adversarial Training ◽

Low Dimensional ◽

Embedding Methods ◽

Local Proximity

As most networks come with some content in each node, attributed network embedding has aroused much research interest. Most existing attributed network embedding methods aim at learning a fixed representation for each node encoding its local proximity. However, those methods usually neglect the global information between nodes distant from each other and distribution of the latent codes. We propose Structural Adversarial Variational Graph Auto-Encoder (SAVGAE), a novel framework which encodes the network structure and node content into low-dimensional embeddings. On one hand, our model captures the local proximity and proximities at any distance of a network by exploiting a high-order proximity indicator named Rooted Pagerank. On the other hand, our method learns the data distribution of each node representation while circumvents the side effect its sampling process causes on learning a robust embedding through adversarial training. On benchmark datasets, we demonstrate that our method performs competitively compared with state-of-the-art models.

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Learning Network Embedding with Community Structural Information

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/407 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yu Li ◽

Ying Wang ◽

Tingting Zhang ◽

Jiawei Zhang ◽

Yi Chang

Keyword(s):

Community Structure ◽

Link Prediction ◽

Structural Information ◽

Representation Learning ◽

Network Embedding ◽

Learning Network ◽

Optimization Framework ◽

Vertex Representation ◽

Low Dimensional ◽

Embedding Methods

Network embedding is an effective approach to learn the low-dimensional representations of vertices in networks, aiming to capture and preserve the structure and inherent properties of networks. The vast majority of existing network embedding methods exclusively focus on vertex proximity of networks, while ignoring the network internal community structure. However, the homophily principle indicates that vertices within the same community are more similar to each other than those from different communities, thus vertices within the same community should have similar vertex representations. Motivated by this, we propose a novel network embedding framework NECS to learn the Network Embedding with Community Structural information, which preserves the high-order proximity and incorporates the community structure in vertex representation learning. We formulate the problem into a principled optimization framework and provide an effective alternating algorithm to solve it. Extensive experimental results on several benchmark network datasets demonstrate the effectiveness of the proposed framework in various network analysis tasks including network reconstruction, link prediction and vertex classification.

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