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https://www.readbyqxmd.com/read/28346243/modeling-flowsheet-data-to-support-secondary-use
#1
Bonnie L Westra, Beverly Christie, Steven G Johnson, Lisiane Pruinelli, Anne LaFlamme, Suzan G Sherman, Jung In Park, Connie W Delaney, Grace Gao, Stuart Speedie
The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring)...
March 24, 2017: Computers, Informatics, Nursing: CIN
https://www.readbyqxmd.com/read/28339684/embedding-nursing-interventions-into-the-world-health-organization-s-international-classification-of-health-interventions-ichi
#2
Nicola Fortune, Nicholas R Hardiker, Gillian Strudwick
Objective: : The International Classification of Health Interventions, currently being developed, seeks to span all sectors of the health system. Our objective was to test the draft classification's coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions. Materials and methods: : A 2-phase content mapping method was used: (1) three coders independently applied the classification to a dataset comprising 100 high-frequency nursing interventions; (2) the coders reached consensus for each intervention and identified reasons for initial discrepancies...
February 11, 2017: Journal of the American Medical Informatics Association: JAMIA
https://www.readbyqxmd.com/read/28337410/damage-to-white-matter-bottlenecks-contributes-to-language-impairments-after-left-hemispheric-stroke
#3
Joseph C Griffis, Rodolphe Nenert, Jane B Allendorfer, Jerzy P Szaflarski
Damage to the white matter underlying the left posterior temporal lobe leads to deficits in multiple language functions. The posterior temporal white matter may correspond to a bottleneck where both dorsal and ventral language pathways are vulnerable to simultaneous damage. Damage to a second putative white matter bottleneck in the left deep prefrontal white matter involving projections associated with ventral language pathways and thalamo-cortical projections has recently been proposed as a source of semantic deficits after stroke...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28335555/activity-recognition-and-semantic-description-for-indoor-mobile-localization
#4
Sheng Guo, Hanjiang Xiong, Xianwei Zheng, Yan Zhou
As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization...
March 21, 2017: Sensors
https://www.readbyqxmd.com/read/28306739/automatic-icd-10-coding-algorithm-using-an-improved-longest-common-subsequence-based-on-semantic-similarity
#5
YunZhi Chen, HuiJuan Lu, LanJuan Li
ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described in patients' medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. This paper presents an improved approach based on the Longest Common Subsequence (LCS) and semantic similarity for automatic Chinese diagnoses, mapping from the disease names given by clinician to the disease names in ICD-10...
2017: PloS One
https://www.readbyqxmd.com/read/28274905/a-learning-health-care-system-using-computer-aided-diagnosis
#6
Amos Cahan, James J Cimino
Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses...
March 8, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/28265499/exploring-biomedical-ontology-mappings-with-graph-theory-methods
#7
Simon Kocbek, Jin-Dong Kim
BACKGROUND: In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies...
2017: PeerJ
https://www.readbyqxmd.com/read/28252395/discriminative-training-of-deep-fully-connected-continuous-crfs-with-task-specific-loss
#8
Fayao Liu, Guosheng Lin, Chunhua Shen
Recent works on deep conditional random fields (CRFs) have set new records on many vision tasks involving structured predictions. Here we propose a fully-connected deep continuous CRFs model with task-specific losses for both discrete and continuous labelling problems. We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNNs), which are jointly learned in an end-to-end fashion...
February 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28244547/the-made-reference-information-model-for-interoperable-pervasive-telemedicine-systems
#9
Nick L S Fung, Valerie M Jones, Hermie J Hermens
OBJECTIVES: The main objective is to develop and validate a reference information model (RIM) to support semantic interoperability of pervasive telemedicine systems. The RIM is one component within a larger, computer-interpretable "MADE language" developed by the authors in the context of the MobiGuide project. To validate our RIM, we applied it to a clinical guideline for patients with gestational diabetes mellitus (GDM). METHODS: The RIM is derived from a generic data flow model of disease management which comprises a network of four types of concurrent processes: Monitoring (M), Analysis (A), Decision (D) and Effectuation (E)...
February 28, 2017: Methods of Information in Medicine
https://www.readbyqxmd.com/read/28244546/structuring-legacy-pathology-reports-by-openehr-archetypes-to-enable-semantic-querying
#10
Stefan Kropf, Peter Krücken, Wolf Mueller, Kerstin Denecke
BACKGROUND: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. OBJECTIVES: We are concentrating on the processing of pathology reports as an example for unstructured clinical documents...
February 28, 2017: Methods of Information in Medicine
https://www.readbyqxmd.com/read/28239634/dataset-for-an-analysis-of-communicative-aspects-of-finance
#11
Natalya Zavyalova
The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis...
April 2017: Data in Brief
https://www.readbyqxmd.com/read/28229468/time-will-tell-a-longitudinal-investigation-of-brain-behavior-relationships-during-reading-development
#12
Mallory C Stites, Sarah Laszlo
ERPs are a powerful tool for the study of reading, as they are both temporally precise and functionally specific. These are essential characteristics for studying a process that unfolds rapidly and consists of multiple, interactive subprocesses. In work with adults, clear, specific models exist linking components of the ERP with individual subprocesses of reading including orthographic decoding, phonological processing, and semantic access (e.g., Grainger & Holcomb, 2009). The relationships between ERP components and reading subprocesses are less clear in development; here, we address two questions regarding these relationships...
February 23, 2017: Psychophysiology
https://www.readbyqxmd.com/read/28229040/predicting-primary-progressive-aphasias-with-support-vector-machine-approaches-in-structural-mri-data
#13
Sandrine Bisenius, Karsten Mueller, Janine Diehl-Schmid, Klaus Fassbender, Timo Grimmer, Frank Jessen, Jan Kassubek, Johannes Kornhuber, Bernhard Landwehrmeyer, Albert Ludolph, Anja Schneider, Sarah Anderl-Straub, Katharina Stuke, Adrian Danek, Markus Otto, Matthias L Schroeter
Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28212087/dual-deep-network-for-visual-tracking
#14
Zhizhen Chi, Hongyang Li, Huchuan Lu, Minghsuan Yang
Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28208126/the-prosody-of-the-czech-discourse-marker-jasn%C3%A4-an-analysis-of-forms-and-functions
#15
Jan Volin, Lenka Weingartová, Oliver Niebuhr
Words like yeah, okay and (al)right are fairly unspecific in their lexical semantics, and not least for this reason there is a general tendency for them to occur with highly varied and expressive prosodic patterns across languages. Here we examine in depth the prosodic forms that express eight pragmatic functions of the Czech discourse marker jasně, including resignation, reassurance, surprise, indifference or impatience. Using a collection of 172 tokens from a corpus of scripted dialogues by 30 native speakers, we performed acoustic analyses, applied classification algorithms and solicited judgments from native listeners in a perceptual experiment...
2016: Phonetica
https://www.readbyqxmd.com/read/28207397/stacked-learning-to-search-for-scene-labeling
#16
Feiyang Cheng, Xuming He, Hong Zhang
Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space. In this paper, a stacked learning to search method is proposed to address scene labeling tasks. We design a simplified search process consisting of a sequence of ranking functions, which are learned based on a stacked learning strategy to prevent over-fitting...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28187898/vessel-segmentation-and-microaneurysm-detection-using-discriminative-dictionary-learning-and-sparse-representation
#17
Malihe Javidi, Hamid-Reza Pourreza, Ahad Harati
Diabetic retinopathy (DR) is a major cause of visual impairment, and the analysis of retinal image can assist patients to take action earlier when it is more likely to be effective. The accurate segmentation of blood vessels in the retinal image can diagnose DR directly. In this paper, a novel scheme for blood vessel segmentation based on discriminative dictionary learning (DDL) and sparse representation has been proposed. The proposed system yields a strong representation which contains the semantic concept of the image...
February 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28185567/from-comorbidities-of-chronic-obstructive-pulmonary-disease-to-identification-of-shared-molecular-mechanisms-by-data-integration
#18
David Gomez-Cabrero, Jörg Menche, Claudia Vargas, Isaac Cano, Dieter Maier, Albert-László Barabási, Jesper Tegnér, Josep Roca
BACKGROUND: Deep mining of healthcare data has provided maps of comorbidity relationships between diseases. In parallel, integrative multi-omics investigations have generated high-resolution molecular maps of putative relevance for understanding disease initiation and progression. Yet, it is unclear how to advance an observation of comorbidity relations (one disease to others) to a molecular understanding of the driver processes and associated biomarkers. RESULTS: Since Chronic Obstructive Pulmonary disease (COPD) has emerged as a central hub in temporal comorbidity networks, we developed a systematic integrative data-driven framework to identify shared disease-associated genes and pathways, as a proxy for the underlying generative mechanisms inducing comorbidity...
November 22, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28114084/binary-set-embedding-for-cross-modal-retrieval
#19
Mengyang Yu, Li Liu, Ling Shao
Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies. In this paper, we propose a novel unsupervised binary coding algorithm called binary set embedding (BSE) to obtain meaningful hash codes for local features from the image domain and words from text domain...
September 27, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28114002/stc-a-simple-to-complex-framework-for-weakly-supervised-semantic-segmentation
#20
Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao, Shuicheng Yan
Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i...
December 6, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
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