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Development and validation of a distributed representation model of Japanese high-dimensional administrative claims data for clinical epidemiology studies

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BMC MEDICAL RESEARCH METHODOLOGY 2025年第1期25卷 1-10页

作者： Matsui, Hiroki Fushimi, Kiyohide Yasunaga, Hideo Univ Tokyo Dept Clin Epidemiol & Hlth Econ Sch Publ Hlth 7-3-1 HongoBunkyo ku Tokyo 1130033 Japan Tokyo Grad Sch Med & Dent Sci Dept Hlth Policy & Informat Inst Sci 1-5-45 YushimaBunkyo Ku Tokyo 1138519 Japan

BackgroundUnmeasured confounders pose challenges when observational data are analysed in comparative effectiveness studies. Integrating high-dimensional administrative claims data may help adjust for unmeasured confounders. We determined whether distributed representations can compress high-dimensional administrative claims data to adjust for unmeasured *** the Japanese Diagnosis Procedure Combination (DPC) database from 1291 hospitals (between April 2018 and March 2020), we applied the word2vec algorithm to create distributed representations for all medical codes. We focused on patients with heart failure (HF) and simulated four risk-adjustment models: 1, no adjustment;2, adjusting for previously reported confounders;3, adjusting for the sum of distributed representation weights of administrative claims data on the day of hospitalisation (novel method);and 4, a combination of models 2 and 3. We re-evaluated a previous study on the effect of early rehabilitation in patients with HF and compared these risk-adjustment methods (models 1-4).Resultsdistributed representations were generated from the data of 15 998 963 in-patients, and 319 581 HF patients were identified. In the simulation study, Model 3 reduced the impact of unmeasured confounders and achieved better covariate balances than Model 1. Model 4 showed no increase in bias compared with the true model (Model 2) and was used as a reference model in the real-world application. When applied to a previous study, models 3 and 4 showed similar *** representation can compress detailed administrative claims data and adjust for unmeasured confounders in comparative effectiveness studies.

关键词： distributed representation Word2vec Administrative claims data High-dimensional propensity score Unmeasured confounder

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distributed representation of knowledge graphs with subgraph-aware proximity

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THEORETICAL COMPUTER SCIENCE 2020年 803卷 48-56页

作者： Han, Xiao Zhang, Chunhong Guo, Chenchen Ji, Yang Hu, Zheng Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

The distributed representation of Knowledge graphs (KGs) aims to embed the original KG into a low-dimensional embedding vector space, so as to facilitate the completion of KGs as well as the application of KGs in other Al fields. Most existing models preserve certain proximity property of KGs in the embedding space, such as the first/second-order proximity and the sequence-aware higher-order proximity. However, the ubiquitous similarity relationship between different sequences has rarely been discussed. In this paper, we propose a unified framework to preserve the subgraph-aware proximity in the embedding space, holding that the sequences within a subgraph generally imply a similar pattern. Especially, according to the composition and structure of KG sequences, we provide three methods for computing the embeddings of KG sequences: 1) Simply adding the involved relations of the KG sequences in a relation subgraph;2) Recurrent neural network for the KG sequences in a complete subgraph;3) Dilated recurrent neural network to match the special structure of the KG sequences in a complete subgraph. Empirically, we evaluate the proposed framework on the KG completion tasks of link prediction and entity classification. The results show that our framework outperforms the baselines by preserving the subgraph-aware proximity. Especially, exploring the special structure of KG sequences can further improve the performance. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Knowledge graph distributed representation Subgraph-aware proximity

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distributed representation OF FUZZY RULES AND ITS APPLICATION TO PATTERN-CLASSIFICATION

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FUZZY SETS AND SYSTEMS 1992年第1期52卷 21-32页

作者： ISHIBUCHI, H NOZAKI, K TANAKA, H Department of Industrial Engineering University of Osaka Prefecture Osaka Japan

This paper introduces the concept of distributed representation of fuzzy rules and applies it to classification problems. distributed representation is implemented by superimposing many fuzzy rules corresponding to different fuzzy partitions of a pattern space. This means that we simultaneously employ many fuzzy rule tables corresponding to different fuzzy partitions in fuzzy inference. In order to apply distributed representation of fuzzy rules to pattern classification problems, we first propose an algorithm to generate fuzzy rules from numerical data. Next we propose a fuzzy inference method using the generated fuzzy rules. The classification power of distributed representation is compared with that of ordinary fuzzy rules which can be viewed as local representation.

关键词： distributed FUZZY RULES distributed representation FUZZY INFERENCE GENERATION OF FUZZY RULES PATTERN CLASSIFICATION

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distributed representation and one-hot representation fusion with gated network for clinical semantic textual similarity

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第1-Sup期20卷 72-72页

作者： Xiong, Ying Chen, Shuai Qin, Haoming Cao, He Shen, Yedan Wang, Xiaolong Chen, Qingcai Yan, Jun Tang, Buzhou Harbin Inst Technol Dept Comp Sci Shenzhen Guangdong Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China Yidu Cloud Beijing Technol Co Ltd Beijing Peoples R China

Background Semantic textual similarity (STS) is a fundamental natural language processing (NLP) task which can be widely used in many NLP applications such as Question Answer (QA), Information Retrieval (IR), etc. It is a typical regression problem, and almost all STS systems either use distributed representation or one-hot representation to model sentence pairs. Methods In this paper, we proposed a novel framework based on a gated network to fuse distributed representation and one-hot representation of sentence pairs. Some current state-of-the-art distributed representation methods, including Convolutional Neural Network (CNN), Bi-directional Long Short Term Memory networks (Bi-LSTM) and Bidirectional Encoder representations from Transformers (BERT), were used in our framework, and a system based on this framework was developed for a shared task regarding clinical STS organized by BioCreative/OHNLP in 2018. Results Compared with the systems only using distributed representation or one-hot representation, our method achieved much higher Pearson correlation. Among all distributed representations, BERT performed best. The highest Person correlation of our system was 0.8541, higher than the best official one of the BioCreative/OHNLP clinical STS shared task in 2018 (0.8328) by 0.0213. Conclusions distributed representation and one-hot representation are complementary to each other and can be fused by gated network.

关键词： Clinical semantic textual similarity Gated network distributed representation One-hot representation

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distributed representation Computation Using CBOW Model and Skip-gram Model 9

Distributed Representation Computation Using CBOW Model and ...

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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Onishi, Takamune Shiina, Hiromitsu Okayama University of Science Graduate School of Informatics Okayama Japan Okayama University of Science Faculty of Informatics Okayama Japan

ISBN: (纸本)9781728173979

Word2Vec is one of methods for generating a distribution representation. Two methods of calculation, CBOW model and skip-gram model, are proposed. However, the skip-gram model has high accuracy of the distribution representation of words, but it takes a long time to learn it. On the other hand, the CBOW model has been shown to be faster but less accurate than the skip-gram model in the distribution representation of words. In this study, we propose a method to combine the CBOW model and the skip-gram model in a way that shares the weight matrix of the CBOW model with the skip-gram model in order to achieve both learning speed and accuracy of the distribution representation of words. © 2020 IEEE.

关键词： CBOW model Combination model distributed representation Skip-gram model Word2Vec

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The distributed representation for Societal Risk Classification toward BBS Posts

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Journal of Systems Science & Complexity 2017年第3期30卷 627-644页

作者： CHEN Jindong TANG Xijin Institute of Systems Science Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China China Academy of Aerospace Systems Science and Engineering Beijing 100048China

The risk classification of BBS posts is important to the evaluation of societal risk level within a period. Using the posts collected from Tianya forum as the data source, the authors adopted the societal risk indicators from socio psychology, and conduct document-level multiple societal risk classification of BBS posts. To effectively capture the semantics and word order of documents, a shallow neural network as Paragraph Vector is applied to realize the distributed vector representations of the posts in the vector space. Based on the document vectors, the authors apply one classification method KNN to identify the societal risk category of the posts. The experimental results reveal that paragraph vector in document-level societal risk classification achieves much faster training speed and at least 10% improvements of F-measures than Bag-of-Words. Furthermore, the performance of paragraph vector is also superior to edit distance and Lucene-based search method. The present work is the first attempt of combining document embedding method with socio psychology research results to public opinions area.

关键词： distributed representation KNN paragraph vector model societal risk classification Tianya forum.

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EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2018年 87卷 49-59页

作者： Zhao, Chao Jiang, Jingchi Guan, Yi Guo, Xitong He, Bin Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Sch Management Harbin 150001 Heilongjiang Peoples R China

Objective: Electronic medical records (EMRs) contain medical knowledge that can be used for clinical decision support (CDS). Our objective is to develop a general system that can extract and represent knowledge contained in EMRs to support three CDS tasks-test recommendation, initial diagnosis, and treatment plan recommendation-given the condition of a patient. Methods: We extracted four kinds of medical entities from records and constructed an EMR-based medical knowledge network (EMKN), in which nodes are entities and edges reflect their co-occurrence in a record. Three bipartite subgraphs (bigraphs) were extracted from the EMKN, one to support each task. One part of the bigraph was the given condition (e.g., symptoms), and the other was the condition to be inferred (e.g., diseases). Each bigraph was regarded as a Markov random field (MRF) to support the inference. We proposed three graph-based energy functions and three likelihood-based energy functions. Two of these functions are based on knowledge representation learning and can provide distributed representations of medical entities. Two EMR datasets and three metrics were utilized to evaluate the performance. Results: As a whole, the evaluation results indicate that the proposed system outperformed the baseline methods. The distributed representation of medical entities does reflect similarity relationships with respect to knowledge level. Conclusion: Combining EMKN and MRF is an effective approach for general medical knowledge representation and inference. Different tasks, however, require individually designed energy functions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Electronic medical record Clinical decision support Medical knowledge network Markov random field distributed representation

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Fine-Grained Unsupervised Temporal Action Segmentation and distributed representation for Skeleton-Based Human Motion Analysis

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 13411-13424页

作者： Ma, Hao Yang, Zaiyue Liu, Haoyang Southern Univ Sci & Technol Shenzhen Key Lab Biomimet Robot & Intelligent Sys Dept Mech & Energy Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Human Augmentat & Rehabil Shenzhen 518055 Peoples R China Beijing Sport Univ Sch Sports Engn Beijing 100091 Peoples R China

Understanding the fine-grained temporal structure of human actions and its semantic interpretation is beneficial to many real-world tasks, such as sports movements, rehabilitation exercises, and daily-life activities analysis. Current action segmentation methods mainly rely on deep neural networks to derive feature embedding of actions from motion data, while works on analyzing human actions in fine-granularity are still lacking due to the lack of clear and generic definitions of subactions and related datasets. On the other hand, the motion representations obtained in current action segmentation methods lack semantic or mathematical interpretability that can be used to evaluate action/subaction similarity in quantitative motion analysis. Toward the goal of fine-grained, interpretable, scalable, and efficient action segmentation, we propose a novel unsupervised action segmentation and distributed representation framework based on intuitive motion primitives defined on pose data. Metrics for comprehensive evaluation of the unsupervised fine-grained action segmentation task performance are proposed, and both public and self-constructed datasets are adopted in the experiments. The results show that the proposed method has good performance and generality across different subjects, datasets, and application scenarios.

关键词： Motion segmentation Trajectory Hidden Markov models Semantics Task analysis Analytical models Sports distributed representation human motion analysis motion primitive (MP) temporal action segmentation

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A Phrase Topic Model Based on distributed representation

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Computers, Materials & Continua 2020年第7期64卷 455-469页

作者： Jialin Ma Jieyi Cheng Lin Zhang Lei Zhou Bolun Chen Jiangsu Internet of Things and Moblie Internet Technology Engineering Laboratory Huaiyin Institute of TechnologyHuai’an223003China University of Fribourg Fribourg1700Switzerland

Traditional topic models have been widely used for analyzing semantic topics from electronic ***,the obvious defects of topic words acquired by them are poor in readability and *** the domain experts are possible to guess their *** fact,phrases are the main unit for people to express *** paper presents a distributed representation-Phrase Latent Dirichlet Allocation(DR-Phrase LDA)which is a phrase topic ***,we reasonably enhance the semantic information of phrases via distributed representation in this *** experimental results show the topics quality acquired by our model is more readable and consistent than other similar topic models.

关键词： Phrase topic model LDA distributed representation Gibbs sampling

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SIGNIFICANCE OF distributed representation IN THE OUTPUT LAYER OF A NEURAL-NETWORK IN A PATTERN-RECOGNITION TASK

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 1994年第1期32卷 77-84页

作者： TAKEDA, T KISHI, K YAMANOUCHI, T MIZOE, H MATSUOKA, T JICHI MED SCH DEPT PHARMACOLMINAMI KAWACHITOCHIGI 32904JAPAN UTSUNOMIYA UNIV FAC ENGNDEPT ELECT & ELECTR ENGNUTSUNOMIYATOCHIGI 321JAPAN

In the cerebral cortex, it is assumed that information is represented by the activity pattern of an assembly of neurons and the synaptic efficacies among then. A distributed representation of pattern is incorporated in the output layer of a neural network with an error back-propagation algorithm, in order to study its technological merits. The network has three layers, which consist of a 32 x 32 array of units (1024) for the input layer, 6-25 units for the hidden layer and 12 units for the output layer. 12 triangular patterns with a variety of parameters are presented to the input layer. Three output-layer units are assigned to each input figure. After initial learning, the network responds to the learned patterns, showing a generalisation for patterns. The network shows resistance to unit de-activation procedures. When the input layer is exposed to the learned pattern, the hidden-layer units show associative activation pattern. These results indicate that the organisation of information representation in the output layer in a neural network strongly influences both the performance of the whole network and information representation in the hidden layer.

关键词： ASSOCIATION BACKPROPAGATION COGNITION distributed representation GENERALIZATION LEARNING NEURAL NETWORK NEURONAL DETERIORATION PATTERN RECOGNITION

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