123 results match your criteria construct dictionary

Constructing Japanese MeSH term dictionaries related to the COVID-19 literature.

Genomics Inform 2021 Sep 30;19(3):e25. Epub 2021 Sep 30.

Computer Science Department, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK.

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. Read More

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September 2021

Learning an Optimal Bipartite Graph for Subspace Clustering via Constrained Laplacian Rank.

IEEE Trans Cybern 2021 Oct 12;PP. Epub 2021 Oct 12.

In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and gene expression analysis. Different from the traditional graph-based methods, co-clustering can utilize the bipartite graph to extract the duality relationship between samples and features. Read More

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October 2021

High-fidelity approximation of grid- and shell-based sampling schemes from undersampled DSI using compressed sensing: Post mortem validation.

Neuroimage 2021 Sep 26;244:118621. Epub 2021 Sep 26.

Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA.

While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. Read More

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September 2021

Passive Fetal Movement Signal Detection System Based on Intelligent Sensing Technology.

J Healthc Eng 2021 25;2021:1745292. Epub 2021 Aug 25.

Department of Artificial Intelligence and Manufacturing, Hechi University, Hechi, China.

Fetal movement (FM) is an essential physiological parameter to determine the health status of the fetus. To address the problems of harrowing FM signal extraction and the low recognition rate of traditional machine learning classifiers in FM signal detection, this paper develops a passive FM signal detection system based on intelligent sensing technology. FM signals are obtained from the abdomen of the pregnant woman by using accelerometers. Read More

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Spatiotemporal structure-aware dictionary learning-based 4D CBCT reconstruction.

Med Phys 2021 Sep 13. Epub 2021 Sep 13.

Institute of Image Processing and Pattern Recognition, School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.

Purpose: Four-dimensional cone-beam computed tomography (4D CBCT) is developed to reconstruct a sequence of phase-resolved images, which could assist in verifying the patient's position and offering information for cancer treatment planning. However, 4D CBCT images suffer from severe streaking artifacts and noise due to the extreme sparse-view CT reconstruction problem for each phase. As a result, it would cause inaccuracy of treatment estimation. Read More

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September 2021

[Analysis of prescription regularity of traditional Chinese medicine for colorectal cancer based on data mining].

Zhongguo Zhong Yao Za Zhi 2021 Aug;46(15):4016-4022

Department of Oncology, the First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou 510405, China.

The tumor prescriptions contained in Dictionary of Tumor Formulas, Compendium of Good Tumor Formulas, Chinese Pharmacopoeia, Ministry of Health Drug Standards for Chinese Medicine Formulas and National Compilation of Standards for Proprietary Chinese Medicines were selected and organized to construct a database for tumor prescriptions, and the data mining techniques were applied to investigate the prescription regularity of colorectal cancer prescriptions. The formula data were extracted after screening in strict accordance with the inclusion and exclusion criteria, and were then analyzed with Microsoft Excel 2010 for frequency statistics, Apriori block provided by SPSS Clementine 12.0 software for correlation rule analysis, and arules and arulesViz packages in R 4. Read More

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Polarization prior to single-photon counting image denoising.

Opt Express 2021 Jul;29(14):21664-21682

Single-photon counting (SPC) imaging technique, which can detect targets in extremely low light levels, has attracted considerable research interest in recent years. To reduce the influence of noise under the low light condition, traditional approaches typically seek various priors from images themselves to construct denoising models, leading to inferior performance as the signal and noise cannot be efficiently distinguished. To address this challenging problem, in this study we propose a novel polarization prior to SPC image denoising based on the observation that a special polarization SPC (PSPC) image has a higher SNR than the SPC image. Read More

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Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.

IEEE Trans Radiat Plasma Med Sci 2021 Jul 26;5(4):537-547. Epub 2020 May 26.

Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.

The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. Read More

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Discriminative Fisher Embedding Dictionary Transfer Learning for Object Recognition.

IEEE Trans Neural Netw Learn Syst 2021 Jun 25;PP. Epub 2021 Jun 25.

In transfer learning model, the source domain samples and target domain samples usually share the same class labels but have different distributions. In general, the existing transfer learning algorithms ignore the interclass differences and intraclass similarities across domains. To address these problems, this article proposes a transfer learning algorithm based on discriminative Fisher embedding and adaptive maximum mean discrepancy (AMMD) constraints, called discriminative Fisher embedding dictionary transfer learning (DFEDTL). Read More

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Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.

Front Neurosci 2021 28;15:679847. Epub 2021 May 28.

School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China.

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. Read More

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Research on Medical Knowledge Graph for Stroke.

J Healthc Eng 2021 24;2021:5531327. Epub 2021 Mar 24.

School of Humanities and Management, Hunan University of Chinese Medicine, Changsha 410208, China.

Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Read More

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October 2021

PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology.

Bioinformatics 2021 Jan 20. Epub 2021 Jan 20.

National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD 20894, USA.

Motivation: Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning. Read More

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January 2021

Text message content as a window into college student drinking: Development and initial validation of a dictionary of "alcohol talk".

Int J Behav Dev 2021 Jan 26;45(1):3-10. Epub 2019 Nov 26.

University of North Carolina at Chapel Hill.

The ubiquity of digital communication within the high-risk drinking environment of college students raises exciting new directions for prevention research. However, we are lacking relevant constructs and tools to analyze digital platforms that serve to facilitate, discuss, and rehash alcohol use. In the current study, we introduce the construct of alcohol-talk (or the extent to which college students use alcohol-related words in text messaging exchanges) as well as introduce and validate a novel tool for measuring this construct. Read More

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January 2021

Language as a window into mind perception: How mental state language differentiates body and mind, human and nonhuman, and the self from others.

J Exp Psychol Gen 2020 Dec 14. Epub 2020 Dec 14.

Kellogg School of Management.

Mind perception-the attribution of mental states to humans and nonhuman entities-is an essential element of social cognition (distinct from related constructs such as perspective-taking and attribution). Despite its importance, research often captures this construct in hypothetical and atypical situations. We therefore used a novel text analysis tool-the Mind Perception Dictionary (MPD)-to measure linguistic use of mind perception (words related to "agency" and "experience") in naturalistic settings (externally valid contexts in the world unprompted by experimental demand) and test basic theoretical claims across 15 total studies (N = 7713). Read More

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December 2020

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.

J Med Internet Res 2020 12 9;22(12):e18526. Epub 2020 Dec 9.

Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.

Background: Common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free text-based pathology reports into the CDM's format. Read More

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December 2020

Developing and validating the self-transcendent emotion dictionary for text analysis.

PLoS One 2020 11;15(9):e0239050. Epub 2020 Sep 11.

School of Communication, Florida State University, Tallahassee, FL, United States of America.

Recent years have seen a growing amount of research effort directed toward what positive media psychologists refer to as self-transcendent emotions, such as awe, admiration, elevation, gratitude, inspiration, and hope. While these emotions are invaluable to promote greater human connectedness, prosociality, and human flourishing, researchers are constrained in terms of analyzing self-transcendent emotions as expressed in spoken and written languages. Drawing upon the word-counting approach of the text analysis paradigm, this project aimed at constructing a dictionary tool-Self-Transcendent Emotion Dictionary (STED)-which can be uploaded into mainstream, text analytic software (e. Read More

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November 2020

Image Target Recognition via Mixed Feature-Based Joint Sparse Representation.

Comput Intell Neurosci 2020 10;2020:8887453. Epub 2020 Aug 10.

School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. Read More

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A benchmark dataset and case study for Chinese medical question intent classification.

BMC Med Inform Decis Mak 2020 07 9;20(Suppl 3):125. Epub 2020 Jul 9.

Inner Mongolia Key Laboratory of Mongolian Information Processing Technology, College of Computer Science, Inner Mongolia Univeristy, University West Road, Hohhot, China.

Background: To provide satisfying answers, medical QA system has to understand the intentions of the users' questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Chinese medical intent classification, and the datasets of other fields are not applicable to the medical QA system. Read More

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Prevalence and context of firearms-related problems in child protective service investigations.

Child Abuse Negl 2020 09 5;107:104572. Epub 2020 Jun 5.

School of Social Work, University of Michigan, 1080 S University, Ann Arbor, MI, 48109, USA.

Background: Despite the significance of firearm safety, we need additional data to understand the prevalence and context surrounding firearm-related problems within the child welfare system.

Objective: Estimate proportion of cases reporting a firearm-related problem during case initiation and the contexts in which these problems exist.

Sample And Setting: 75,809 caseworker-written investigation summaries that represented all substantiated referrals of maltreatment in Michigan from 2015 to 2017. Read More

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September 2020

Keyword extraction and structuralization of medical reports.

Health Inf Sci Syst 2020 Dec 3;8(1):18. Epub 2020 Apr 3.

3Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.

Purpose: In recent years, patients usually accept more accurate and detailed examinations because of the rapid advances in medical technology. Many of the examination reports are not represented in numerical data, but text documents written by the medical examiners based on the observations from the instruments and biochemical tests. If the above-mentioned unstructured data can be organized as a report in a structured form, it will help doctors to understand a patient's status of the various examinations more efficiently. Read More

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December 2020

An improved deep network for tissue microstructure estimation with uncertainty quantification.

Med Image Anal 2020 04 22;61:101650. Epub 2020 Jan 22.

Department of Radiology, Peking University Third Hospital, Beijing, China.

Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it is beneficial to exploit the sparsity of diffusion signals jointly in the spatial and angular domains, and the deep network can be designed by unfolding iterative processes that adaptively incorporate historical information for sparse reconstruction. Read More

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Provider Perspectives on the Feasibility and Utility of Routine Patient-Reported Outcomes Assessment in Heart Failure: A Qualitative Analysis.

J Am Heart Assoc 2020 01 15;9(2):e013047. Epub 2020 Jan 15.

University of Utah School of Medicine Salt Lake City UT.

Background Patient-reported outcomes (PROs) objectively measure health-related quality of life and provide prognostic information. Advances in technology now allow for rapid, patient-friendly PRO assessment and scoring, yet the adoption of PROs in clinic has been slow. We conducted a multicenter qualitative study of diverse providers to describe the barriers and facilitators of routine PRO use in heart failure clinics. Read More

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January 2020

Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation.

Comput Intell Neurosci 2019 21;2019:8258275. Epub 2019 Nov 21.

School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China.

An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Read More

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The lexical fallacy in emotion research: Mistaking vernacular words for psychological entities.

Alan Page Fiske

Psychol Rev 2020 01 4;127(1):95-113. Epub 2019 Nov 4.

University of California, Los Angeles.

Vernacular lexemes appear self-evident, so we unwittingly reify them. But the words and phrases of natural languages comprise a treacherous basis for identifying valid psychological constructs, as I illustrate in emotion research. Like other vernacular lexemes, the emotion labels in natural languages do not have definite, stable, mutually transparent meanings, and any one vernacular word may be used to denote multiple scientifically distinct entities. Read More

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January 2020

Ontology-based clinical information extraction from physician's free-text notes.

J Biomed Inform 2019 10 29;98:103276. Epub 2019 Aug 29.

Information Systems Department, Faculty of Computers and Information, Helwan University, Helwan, Cairo, Egypt.

Documenting clinical notes in electronic health records might affect physician's workflow. In this paper, an Ontology-based clinical information extraction system, OB-CIE, has been developed. OB-CIE system provides a method for extracting clinical concepts from physician's free-text notes and converts the unstructured clinical notes to structured information to be accessed in electronic health records. Read More

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October 2019

Blind Deblurring of Text Images Using a Text-Specific Hybrid Dictionary.

IEEE Trans Image Process 2019 Aug 13. Epub 2019 Aug 13.

In this paper, we propose a blind text image deblurring algorithm by using a text-specific hybrid dictionary. After careful analysis, we find that the text-specific hybrid dictionary has the great ability of providing powerful contextual information for text image deblurring. Here, it is worth noting that our proposed method is inspired by our observation that an intermediate latent image contains not only sharp regions, but also multiple types of small blurred regions. Read More

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Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations.

IEEE Trans Pattern Anal Mach Intell 2020 12 3;42(12):3119-3135. Epub 2020 Nov 3.

This work studies the problem of learning appropriate low dimensional image representations. We propose a generic algorithmic framework, which leverages two classic representation learning paradigms, i.e. Read More

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December 2020

Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation.

Hum Brain Mapp 2019 09 21;40(13):3860-3880. Epub 2019 May 21.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

4D (spatial + temporal) infant cortical surface atlases covering dense time points are highly needed for understanding dynamic early brain development. In this article, we construct a set of 4D infant cortical surface atlases with longitudinally consistent and sharp cortical attribute patterns at 11 time points in the first six postnatal years, that is, at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months of age, which is targeted for better normalization of the dynamic changing early brain cortical surfaces. To ensure longitudinal consistency and unbiasedness, we adopt a two-stage group-wise surface registration. Read More

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September 2019

Deterministic Measurement Matrix for Compressive Imaging Using Novel Transform Functions.

IEEE Trans Image Process 2019 May 7. Epub 2019 May 7.

Efficient source coding is desired for any data storage and transmission. It could be enabled by adopting a transform inspired by natural phenomena. Based on the mechanical vibration models, a family of bases applicable to data compression is constructed. Read More

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Discriminative Fisher Embedding Dictionary Learning Algorithm for Object Recognition.

IEEE Trans Neural Netw Learn Syst 2020 Mar 30;31(3):786-800. Epub 2019 Apr 30.

Both interclass variances and intraclass similarities are crucial for improving the classification performance of discriminative dictionary learning (DDL) algorithms. However, existing DDL methods often ignore the combination between the interclass and intraclass properties of dictionary atoms and coding coefficients. To address this problem, in this paper, we propose a discriminative Fisher embedding dictionary learning (DFEDL) algorithm that simultaneously establishes Fisher embedding models on learned atoms and coefficients. Read More

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