Journal articles: 'Disease representation'

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Odaibo, Stephen Gbejule. "Representation of Disease." JAMA Ophthalmology 133, no. 5 (May 1, 2015): 618. http://dx.doi.org/10.1001/jamaophthalmol.2014.5655.

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Xuan, Sun, Wang, Zhang, and Pan. "Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks." International Journal of Molecular Sciences 20, no. 15 (July 25, 2019): 3648. http://dx.doi.org/10.3390/ijms20153648.

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Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

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Malhotra, Raman, and Jane M. Olver. "Diagrammatic representation of lacrimal disease." Eye 14, no. 3 (May 2000): 358–63. http://dx.doi.org/10.1038/eye.2000.88.

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Perciaccante, Antonio, Philippe Charlier, Alessia Coralli, Saudamini Deo, Otto Appenzeller, and Raffaella Bianucci. "Exploring Disease Representation in Movies." Journal of General Internal Medicine 34, no. 11 (August 13, 2019): 2351–54. http://dx.doi.org/10.1007/s11606-019-05254-6.

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Brandão, Brígida Maria Gonçalves de Melo, Rebeca Coelho de Moura Angelim, Sergio Corrêa Marques, Denize Cristina de Oliveira, Regina Célia de Oliveira, and Fátima Maria da Silva Abrão. "Social representations of the elderly about HIV/AIDS." Revista Brasileira de Enfermagem 72, no. 5 (October 2019): 1349–55. http://dx.doi.org/10.1590/0034-7167-2018-0296.

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ABSTRACT Objective: to understand the representational content about HIV/AIDS among seropositive elderly people. Method: a qualitative study carried out from April to May 2017, in the city of Recife/PE, with 48 seropositive elderly people, through a semi-structured interview. The Social Representations Theory was used as theoretical framework and the method of lexical analysis through IRAMUTEQ software. Results: it was observed that the social representation of HIV is structured around the proximity of death and that it is a disease of restricted groups, leading to feelings of sadness. On the other hand, it is evident a transformation of the representation linked to the reified knowledge, leading to the process of naturalization of the disease. Final considerations: it is concluded that the elderly living with HIV, when they undergo a process of reframing about the disease, become more flexible to deal with their condition of seropositivity.

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Przelaskowski, Artur. "Semantic Sparse Representation of Disease Patterns." International Journal of Electronics and Telecommunications 56, no. 3 (September 1, 2010): 273–80. http://dx.doi.org/10.2478/v10177-010-0036-x.

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Semantic Sparse Representation of Disease PatternsSparse data representation is discussed in a context of useful fundamentals led to semantic content description and extraction of information. Disease patterns as semantic information extracted from medical images were underlined because of discussed application of computer-aided diagnosis. Compressive sensing rules were adjusted to the requirements of diagnostic pattern recognition. Proposed methodology of sparse disease patterns considers accuracy of sparse representation to estimate target content for detailed analysis. Semantics of sparse representation were modeled by morphological content analysis. Subtle or hidden components were extracted and displayed to increase information completeness. Usefulness of sparsity was verified for computer-aided diagnosis of stroke based on brain CT scans. Implemented method was based on selective and sparse representation of subtle hypodensity to improve diagnosis. Visual expression of disease signatures was fixed to radiologist requirements, domain knowledge and experimental analysis issues. Diagnosis assistance suitability was proven by experimental subjective rating and automatic recognition.

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YANG, YunSheng, HuiJie FAN, YanDong TANG, and Yang CONG. "Sparse representation for gastropathy disease diagnosis." Chinese Science Bulletin 58, S2 (January 1, 2013): 145–51. http://dx.doi.org/10.1360/972013-950.

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Li, Yuhua, Zhihui Luo, Fengjie Wang, and Yingxu Wang. "Hyperspectral Leaf Image-Based Cucumber Disease Recognition Using the Extended Collaborative Representation Model." Sensors 20, no. 14 (July 21, 2020): 4045. http://dx.doi.org/10.3390/s20144045.

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Collaborative representation (CR)-based classification has been successfully applied to plant disease recognition in cases with sufficient training samples of each disease. However, collecting enough training samples is usually time consuming and labor-intensive. Moreover, influenced by the non-ideal measurement environment, samples may be corrupted by variables introduced by bad illumination and occlusions of adjacent leaves. Consequently, an extended collaborative representation (ECR)-based classification model is presented in this paper. Then, it is applied to cucumber leaf disease recognition, which constructs a pure spectral library consisting of several representative samples for each disease and designs a universal variation spectral library that deals with linear variables superimposed on samples. Thus, each query sample is encoded as a linear combination of atoms from these two spectral libraries and disease identity is determined by the disease of minimal reconstruction residuals. Experiments are conducted on spectral curves extracted from normal leaves and the disease lesions of leaves infected with cucumber anthracnose and brown spot. The diagnostic accuracy is higher than 94.7% and the average online diagnosis time is short, about 1 to 1.3 ms. The results indicate that the ECR-based classification model is feasible in the fast and accurate diagnosis of cucumber leaf diseases.

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Bakhronova, Matluba Akhmedovna. "REPRESENTATION OF DISEASE NAMES IN THE LITERATURE." Theoretical & Applied Science 86, no. 06 (June 30, 2020): 141–44. http://dx.doi.org/10.15863/tas.2020.06.86.27.

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Grossman, Murray, Ryan Murray, Phyllis Koenig, Sherry Ash, Katy Cross, Peachie Moore, and Vanessa Troiani. "Verb acquisition and representation in Alzheimer's disease." Neuropsychologia 45, no. 11 (January 2007): 2508–18. http://dx.doi.org/10.1016/j.neuropsychologia.2007.03.020.

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Feng, Qingxiang, and Yicong Zhou. "Kernel Combined Sparse Representation for Disease Recognition." IEEE Transactions on Multimedia 18, no. 10 (October 2016): 1956–68. http://dx.doi.org/10.1109/tmm.2016.2602062.

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Thomas, R. W. "Some Spatial Representation Problems in Disease Modeling." Geographical Analysis 22, no. 3 (September 3, 2010): 209–23. http://dx.doi.org/10.1111/j.1538-4632.1990.tb00206.x.

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Allain, Philippe, Didier Le Gall, Céline Foucher, Frédérique Etcharry-Bouyx, Jean Barré, Frédéric Dubas, and Gilles Berrut. "Script representation in patients with Alzheimer's disease." Cortex 44, no. 3 (March 2008): 294–304. http://dx.doi.org/10.1016/j.cortex.2006.07.003.

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Vernon, Matthew C., and Matt J. Keeling. "Representing the UK's cattle herd as static and dynamic networks." Proceedings of the Royal Society B: Biological Sciences 276, no. 1656 (October 14, 2008): 469–76. http://dx.doi.org/10.1098/rspb.2008.1009.

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Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using stochastic disease simulations, a wide range of network representations of the UK cattle herd are compared. We find that the simpler static network representations are often deficient when compared with a fully dynamic representation, and should therefore be used only with caution in epidemiological modelling. In particular, due to temporal structures within the dynamic network, static networks consistently fail to capture the predicted epidemic behaviour associated with dynamic networks even when parameterized to match early growth rates.

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Jha, Kishlay, Guangxu Xun, and Aidong Zhang. "Continual representation learning for evolving biomedical bipartite networks." Bioinformatics 37, no. 15 (February 3, 2021): 2190–97. http://dx.doi.org/10.1093/bioinformatics/btab067.

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Abstract Motivation Many real-world biomedical interactions such as ‘gene-disease’, ‘disease-symptom’ and ‘drug-target’ are modeled as a bipartite network structure. Learning meaningful representations for such networks is a fundamental problem in the research area of Network Representation Learning (NRL). NRL approaches aim to translate the network structure into low-dimensional vector representations that are useful to a variety of biomedical applications. Despite significant advances, the existing approaches still have certain limitations. First, a majority of these approaches do not model the unique topological properties of bipartite networks. Consequently, their straightforward application to the bipartite graphs yields unsatisfactory results. Second, the existing approaches typically learn representations from static networks. This is limiting for the biomedical bipartite networks that evolve at a rapid pace, and thus necessitate the development of approaches that can update the representations in an online fashion. Results In this research, we propose a novel representation learning approach that accurately preserves the intricate bipartite structure, and efficiently updates the node representations. Specifically, we design a customized autoencoder that captures the proximity relationship between nodes participating in the bipartite bicliques (2 × 2 sub-graph), while preserving both the global and local structures. Moreover, the proposed structure-preserving technique is carefully interleaved with the central tenets of continual machine learning to design an incremental learning strategy that updates the node representations in an online manner. Taken together, the proposed approach produces meaningful representations with high fidelity and computational efficiency. Extensive experiments conducted on several biomedical bipartite networks validate the effectiveness and rationality of the proposed approach.

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Huang, Yanqun, Ni Wang, Zhiqiang Zhang, Honglei Liu, Xiaolu Fei, Lan Wei, and Hui Chen. "Patient Representation From Structured Electronic Medical Records Based on Embedding Technique: Development and Validation Study." JMIR Medical Informatics 9, no. 7 (July 23, 2021): e19905. http://dx.doi.org/10.2196/19905.

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Background The secondary use of structured electronic medical record (sEMR) data has become a challenge due to the diversity, sparsity, and high dimensionality of the data representation. Constructing an effective representation for sEMR data is becoming more and more crucial for subsequent data applications. Objective We aimed to apply the embedding technique used in the natural language processing domain for the sEMR data representation and to explore the feasibility and superiority of the embedding-based feature and patient representations in clinical application. Methods The entire training corpus consisted of records of 104,752 hospitalized patients with 13,757 medical concepts of disease diagnoses, physical examinations and procedures, laboratory tests, medications, etc. Each medical concept was embedded into a 200-dimensional real number vector using the Skip-gram algorithm with some adaptive changes from shuffling the medical concepts in a record 20 times. The average of vectors for all medical concepts in a patient record represented the patient. For embedding-based feature representation evaluation, we used the cosine similarities among the medical concept vectors to capture the latent clinical associations among the medical concepts. We further conducted a clustering analysis on stroke patients to evaluate and compare the embedding-based patient representations. The Hopkins statistic, Silhouette index (SI), and Davies-Bouldin index were used for the unsupervised evaluation, and the precision, recall, and F1 score were used for the supervised evaluation. Results The dimension of patient representation was reduced from 13,757 to 200 using the embedding-based representation. The average cosine similarity of the selected disease (subarachnoid hemorrhage) and its 15 clinically relevant medical concepts was 0.973. Stroke patients were clustered into two clusters with the highest SI (0.852). Clustering analyses conducted on patients with the embedding representations showed higher applicability (Hopkins statistic 0.931), higher aggregation (SI 0.862), and lower dispersion (Davies-Bouldin index 0.551) than those conducted on patients with reference representation methods. The clustering solutions for patients with the embedding-based representation achieved the highest F1 scores of 0.944 and 0.717 for two clusters. Conclusions The feature-level embedding-based representations can reflect the potential clinical associations among medical concepts effectively. The patient-level embedding-based representation is easy to use as continuous input to standard machine learning algorithms and can bring performance improvements. It is expected that the embedding-based representation will be helpful in a wide range of secondary uses of sEMR data.

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Campos, Caroline Gonçalves Pustiglione, Maria de Fátima Mantovani, Maria Elisa Brum do Nascimento, and Cristiam Carla Cassi. "Social representations of illness among people with chronic kidney disease." Revista Gaúcha de Enfermagem 36, no. 2 (June 2015): 106–12. http://dx.doi.org/10.1590/1983-1447.2015.02.48183.

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OBJECTIVE: To describe the social representations of illness among people with chronic kidney disease undergoing haemodialysis. METHOD: Descriptive, qualitative research, anchored on the social representations theory. This study was conducted in the municipality of Ponta Grossa, Paraná State, Brazil, with 23 adults with chronic kidney disease. Data were collection between February and November 2012 by means of a semi-structured interview, and analyzed using Content Analysis. RESULTS: The interviews led to the categories "the meaning of kidney disease": awareness of finitude, and "survival": the visible with chronic kidney disease. The representation of illness unveiled a difference and interruption in life projects, and haemodialysis meant loss of freedom, imprisonment and stigma. CONCLUSION: Family ties and the individuals´ social role are determining representations for healthcare.

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Xuan, Pan, Zhang, Liu, and Sun. "Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations." Cells 8, no. 9 (August 30, 2019): 1012. http://dx.doi.org/10.3390/cells8091012.

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Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

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Xuan, Ping, Nan Sheng, Tiangang Zhang, Yong Liu, and Yahong Guo. "CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA–Disease Associations." International Journal of Molecular Sciences 20, no. 17 (August 30, 2019): 4260. http://dx.doi.org/10.3390/ijms20174260.

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It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.

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Pulsipher, Kayd J., Mindy D. Szeto, Chandler W. Rundle, Colby L. Presley, Melissa R. Laughter, and Robert P. Dellavalle. "Global Burden of Skin Disease Representation in the Literature: Bibliometric Analysis." JMIR Dermatology 4, no. 2 (August 31, 2021): e29282. http://dx.doi.org/10.2196/29282.

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Background The global burden of skin disease may be reduced through research efforts focused on skin diseases with the highest reported disability-adjusted life years. Objective This study evaluates the representation of dermatologic conditions comprising the highest disability-adjusted life years in dermatology literature to identify areas that could benefit from greater research focus. Methods The top 10 skin disorders according to their respective disability-adjusted life years as per the 2013 Global Burden of Disease were identified using previous studies. The top 5 dermatology journals ranked by the 2019 h-index were also identified. A PubMed search of each journal was performed using individual skin disease terms. From 2015 to 2020, all indexed publications pertaining to each disease were recorded and compared to the total number of publications for each journal surveyed. Results A total of 19,727 papers were published in the 5 journals over the span of 2015-2020. Although melanoma ranked as the eighth highest in disability-adjusted life years, it had the highest representation in the literature (1995/19,727, 10.11%). Melanoma was followed in representation by psoriasis (1936/19,727, 9.81%) and dermatitis (1927/19,727, 9.77%). These 3 conditions comprised a total of 29.69% (5858/19,727) of the total publications, while the remaining 7 skin conditions were represented by a combined 6.79% (1341/19,727) of the total publications. Conclusions This research identifies gaps in the literature related to the top skin diseases contributing to the global burden of disease. Our study provides insight into future opportunities of focused research on less-studied skin diseases to potentially aid in reducing the global burden of skin disease.

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Xuan, Ping, Lianfeng Zhao, Tiangang Zhang, Yilin Ye, and Yan Zhang. "Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit." Molecules 24, no. 15 (July 25, 2019): 2712. http://dx.doi.org/10.3390/molecules24152712.

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Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.

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Kwak, Kichang, Hyuk Jin Yun, Gilsoon Park, and Jong-Min Lee. "Multi-Modality Sparse Representation for Alzheimer’s Disease Classification." Journal of Alzheimer's Disease 65, no. 3 (September 11, 2018): 807–17. http://dx.doi.org/10.3233/jad-170338.

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Sá Couto, D., W. L. van Meurs, and J. A. Goodwin. "Graphical and mathematical representation of congenital heart disease." European Journal of Anaesthesiology 20, no. 10 (October 2003): 841–42. http://dx.doi.org/10.1097/00003643-200310000-00017.

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Kagerer, Florian A., Jeff J. Summers, Winston D. Byblow, and Bruce Taylor. "Altered corticomotor representation in patients with Parkinson's disease." Movement Disorders 18, no. 8 (July 24, 2003): 919–27. http://dx.doi.org/10.1002/mds.10452.

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Kim, Sinjeong. "Representation of Disease in the Joseon Yadam and Two gaze about disease." Journal of Korean Oral Literature 58 (September 30, 2020): 183–210. http://dx.doi.org/10.22274/koralit.2020.58.006.

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Rivera, Eleanor, Colleen Corte, Alana Steffen, Holli DeVon, Eileen Collins, and Pamela McCabe. "Illness Representation and Self-Care Ability in Older Adults with Chronic Disease." Geriatrics 3, no. 3 (July 31, 2018): 45. http://dx.doi.org/10.3390/geriatrics3030045.

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Chronic illness affects >50% of adults in the United States and accounts for >80% of healthcare spending. The purpose of this study was to determine whether beliefs about one’s chronic disease (illness representation) are associated with self-care activation, emergency department (ED) visits, or hospitalizations. Using a cross-sectional design, we recruited older adults with heart failure, chronic obstructive pulmonary disease (COPD), and chronic kidney disease. The Revised Illness Perception Questionnaire (IPQ-R) measured perceptions about disease. The Patient Activation Measure measured self-care activation. ED visits and hospitalizations were measured by self-report. IPQ-R scores were analyzed using latent profile analysis to identify subgroups. Participants included 187 adults (mean age 65 years, 54% female, 74% Black). We found three subgroups (stable, overwhelmed, and confident). Groups did not differ demographically or by disease. The stable group (few consequences, non-fluctuating pattern) had the fewest hospitalizations. The overwhelmed group (many consequences, fluctuating pattern, high negative emotion) had high hospitalizations and low self-care ability. The confident group (high disease control, well-understood) had the highest self-care ability, but also high hospitalizations. ED visits did not differ by group. We found three subgroups that differ in their illness representation and health outcomes. Findings suggest that assessing patients’ illness representations may have important implications for subgroup-specific interventions.

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Kharlamenkova, Natal’ya Ye. "DISEASE REPRESENTATION AND ITS RELATIONSHIP WITH THE COPING STYLES IN ADOLESCENTS WITH TUMOURS OF THE MUSCULOSKELETAL SYSTEM." Vestnik Kostroma State University. Series: Pedagogy. Psychology. Sociokinetics, no. 2 (2020): 134–40. http://dx.doi.org/10.34216/2073-1426-2020-26-2-134-140.

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The investigation results of the disease representation and its relationship with the coping styles in adolescents (n = 43) with tumour of the musculoskeletal system are discussed. The data obtained before and after surgery for the removal of the musculoskeletal tumour were compared. The results expected in accordance with the hypothesis that estimates of physical and emotional states dominate in adolescents disease representations have not been confirmed. It is shown that disease representation correlates with the social support and coping resources (motivation for recovery) and practically does not include the characteristics of the child’s physical and emotional states. A comparison of the relationship between disease representation and coping styles in subgroups of adolescents with different levels of stress revealed the following differences: with a low level of stress, the motivation for recovery as the child’s internal resource is correlated with an active search for social support which at the stage after surgery, begins to be supported by different styles of coping behaviour – solving the problem and reference to others; intense experience of stress significantly limits the possibilities of a teenager which correlates its disease with the physical and emotional problems solved by passively waiting for help from loved ones. Own resources to cope with difficult life situations in adolescents with high levels of stress are not widely available.

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Dawes, James. "Narrating Disease: Aids, Consent, and the Ethics of Representation." Social Text, no. 43 (1995): 27. http://dx.doi.org/10.2307/466625.

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Förster, Stefan, Andreas Vaitl, Stefan J. Teipel, Igor Yakushev, Mona Mustafa, Christian la Fougère, Axel Rominger, et al. "Functional Representation of Olfactory Impairment in Early Alzheimer's Disease." Journal of Alzheimer's Disease 22, no. 2 (October 1, 2010): 581–91. http://dx.doi.org/10.3233/jad-2010-091549.

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Mazein, Alexander, Richard G. Knowles, Ian Adcock, Kian Fan Chung, Craig E. Wheelock, Anke H. Maitland-van der Zee, Peter J. Sterk, and Charles Auffray. "AsthmaMap: An expert-driven computational representation of disease mechanisms." Clinical & Experimental Allergy 48, no. 8 (July 30, 2018): 916–18. http://dx.doi.org/10.1111/cea.13211.

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Okada, M. "Knowledge representation and compilation for symptom-disease-test relationships." IEEE Transactions on Biomedical Engineering 36, no. 5 (May 1989): 547–51. http://dx.doi.org/10.1109/10.24257.

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Diaz, Moises, Miguel Angel Ferrer, Donato Impedovo, Giuseppe Pirlo, and Gennaro Vessio. "Dynamically enhanced static handwriting representation for Parkinson’s disease detection." Pattern Recognition Letters 128 (December 2019): 204–10. http://dx.doi.org/10.1016/j.patrec.2019.08.018.

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Yang, Kuo, Ruyu Wang, Guangming Liu, Zixin Shu, Ning Wang, Runshun Zhang, Jian Yu, Jianxin Chen, Xiaodong Li, and Xuezhong Zhou. "HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction." IEEE Journal of Biomedical and Health Informatics 23, no. 4 (July 2019): 1805–15. http://dx.doi.org/10.1109/jbhi.2018.2870728.

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Spiro, Adam, Jonatan Fernández García, and Chen Yanover. "Inferring new relations between medical entities using literature curated term co-occurrences." JAMIA Open 2, no. 3 (July 1, 2019): 378–85. http://dx.doi.org/10.1093/jamiaopen/ooz022.

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Abstract Objectives Identifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations. Materials and Methods We demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression. Results These trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation. Discussion Selecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types. Conclusion The discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.

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Zadrozny, Sara. "Women’s Ageing as Disease." Humanities 8, no. 2 (April 15, 2019): 75. http://dx.doi.org/10.3390/h8020075.

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In the medical humanities, there has been a growing interest in diagnosing disease in fictional characters, particularly with the idea that characters in Charles Dickens’s novels may be suffering from diseases recognised today. However, an area that deserves greater attention is the representation of women’s ageing as disease in Victorian literature and medical narratives. Even as Victorian doctors were trying to cure age-related illnesses, they continued to employ classical notions of unhealthy female ageing. For all his interest in medical matters, the novelist Charles Dickens wrote about old women in a similar vein. Using close reading to analyse Victorian gerontology alongside Charles Dickens’s novels Dombey and Son (1848) and Great Expectations (1861), this article examines narratives of female ageing as disease. It concludes by pointing to the ways that Victorian gerontology impacts on how we view women’s ageing as ‘diseased’ today.

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Péres, Denise Siqueira, Laércio Joel Franco, Manoel Antônio dos Santos, and Maria Lúcia Zanetti. "Social representations of low-income diabetic women according to the health-disease process." Revista Latino-Americana de Enfermagem 16, no. 3 (June 2008): 389–95. http://dx.doi.org/10.1590/s0104-11692008000300009.

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The purpose of this article is to identify the social representations of low-income diabetic women according to the health-disease process. This is a descriptive, exploratory study. Eight participants, all of them patients at a basic health unit in Ribeirão Preto, were interviewed in 2003. The data were organized according to thematic content analysis and analyzed according to theory of social representations. Diabetes is related to negative feelings, such as shock, anger and sadness; the diet plan is linked to the loss of pleasure, and also to health risks. The diabetic women showed an ambivalent relation to medication, perceived it as both tiring and as a resource that promotes well-being and improvements in quality of life. The negative representation of health services seems to interfere with the behavior of adherence to pharmacological treatment. Understanding the representations of women with diabetes contributes to integral healthcare for diabetic patients.

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Yan, Yan, Kamen Ivanov, Olatunji Mumini Omisore, Tobore Igbe, Qiuhua Liu, Zedong Nie, and Lei Wang. "Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation." Sensors 20, no. 7 (April 3, 2020): 2006. http://dx.doi.org/10.3390/s20072006.

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Neuro-degenerative disease is a common progressive nervous system disorder that leads to serious clinical consequences. Gait rhythm dynamics analysis is essential for evaluating clinical states and improving quality of life for neuro-degenerative patients. The magnitude of stride-to-stride fluctuations and corresponding changes over time—gait dynamics—reflects the physiology of gait, in quantifying the pathologic alterations in the locomotor control system of health subjects and patients with neuro-degenerative diseases. Motivated by algebra topology theory, a topological data analysis-inspired nonlinear framework was adopted in the study of the gait dynamics. Meanwhile, the topological representation–persistence landscapes were used as input of classifiers in order to distinguish different neuro-degenerative disease type from healthy. In this work, stride-to-stride time series from healthy control (HC) subjects are compared with the gait dynamics from patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and Parkinson’s disease (PD). The obtained results show that the proposed methodology discriminates healthy subjects from subjects with other neuro-degenerative diseases with relatively high accuracy. In summary, our study is the first attempt to provide a topological representation-based method into the disease classification with gait rhythms measured from the stride intervals to visualize gait dynamics and classify neuro-degenerative diseases. The proposed method could be potentially used in earlier interventions and state monitoring.

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Trofimov, Assya, Joseph Paul Cohen, Yoshua Bengio, Claude Perreault, and Sébastien Lemieux. "Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition." Bioinformatics 36, Supplement_1 (July 1, 2020): i417—i426. http://dx.doi.org/10.1093/bioinformatics/btaa488.

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Abstract Motivation The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations. Results We present the factorized embeddings (FE) model, a self-supervised deep learning algorithm that learns simultaneously, by tensor factorization, gene and sample representation spaces. We ran the model on RNA-Seq data from two large-scale cohorts and observed that the sample representation captures information on single gene and global gene expression patterns. Moreover, we found that the gene representation space was organized such that tissue-specific genes, highly correlated genes as well as genes participating in the same GO terms were grouped. Finally, we compared the vector representation of samples learned by the FE model to other similar models on 49 regression tasks. We report that the representations trained with FE rank first or second in all of the tasks, surpassing, sometimes by a considerable margin, other representations. Availability and implementation A toy example in the form of a Jupyter Notebook as well as the code and trained embeddings for this project can be found at: https://github.com/TrofimovAssya/FactorizedEmbeddings. Supplementary information Supplementary data are available at Bioinformatics online.

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Coden, Anni, Guergana Savova, Igor Sominsky, Michael Tanenblatt, James Masanz, Karin Schuler, James Cooper, Wei Guan, and Piet C. de Groen. "Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model." Journal of Biomedical Informatics 42, no. 5 (October 2009): 937–49. http://dx.doi.org/10.1016/j.jbi.2008.12.005.

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Orr, David MR. "Dementia and detectives: Alzheimer’s disease in crime fiction." Dementia 19, no. 3 (May 28, 2018): 560–73. http://dx.doi.org/10.1177/1471301218778398.

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Fictional representations of dementia have burgeoned in recent years, and scholars have amply explored their double-edged capacity to promote tragic perspectives or normalising images of ‘living well’ with the condition. Yet to date, there has been only sparse consideration of the treatment afforded dementia within the genre of crime fiction. Focusing on two novels, Emma Healey’s Elizabeth is Missing and Alice LaPlante’s Turn of Mind, this article considers what it means in relation to the ethics of representation that these authors choose to cast as their amateur detective narrators women who have dementia. Analysing how their narrative portrayals frame the experience of living with dementia, it becomes apparent that features of the crime genre inflect the meanings conveyed. While aspects of the novels may reinforce problem-based discourses around dementia, in other respects they may spur meaningful reflection about it among the large readership of this genre.

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Smith, Roger, and Sander L. Gilman. "Disease and Representation: Images of Illness from Madness to AIDS." American Historical Review 95, no. 4 (October 1990): 1152. http://dx.doi.org/10.2307/2163500.

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Hashimoto, R., N. Komori, A. Tagawa, T. Ogawa, H. Katoh, and W. Yumura. "Egocentric and allocentric spatial representation in aMCI and Alzheimer’s disease." Journal of the Neurological Sciences 381 (October 2017): 329. http://dx.doi.org/10.1016/j.jns.2017.08.934.

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Maguire, C. P., R. Coen, D. O'Neill, B. Lawlor, J. B. Walsh, M. Rowan, and D. Coakley. "Electrophysiological Representation of a Mneumonic Demand Test in Alzheimer's Disease." Age and Ageing 27, suppl 2 (January 1, 1998): 38. http://dx.doi.org/10.1093/ageing/27.suppl_2.38-a.

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Kostek, Bożena, Adam Kupryjanow, and Andrzej Czyżewski. "Knowledge representation of motor activity of patients with Parkinson’s disease." Natural Computing 14, no. 4 (December 31, 2014): 579–91. http://dx.doi.org/10.1007/s11047-014-9475-0.

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Li, Jinxing, Bob Zhang, and David Zhang. "Joint discriminative and collaborative representation for fatty liver disease diagnosis." Expert Systems with Applications 89 (December 2017): 31–40. http://dx.doi.org/10.1016/j.eswa.2017.07.023.

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Zhang, Shanwen, Xiaowei Wu, Zhuhong You, and Liqing Zhang. "Leaf image based cucumber disease recognition using sparse representation classification." Computers and Electronics in Agriculture 134 (March 2017): 135–41. http://dx.doi.org/10.1016/j.compag.2017.01.014.

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Scarpina, Federica, Francesca Giulia Magnani, Sofia Tagini, Lorenzo Priano, Alessandro Mauro, and Anna Sedda. "Mental representation of the body in action in Parkinson’s disease." Experimental Brain Research 237, no. 10 (July 20, 2019): 2505–21. http://dx.doi.org/10.1007/s00221-019-05608-w.

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Henderson, Victor W. "INTRODUCTION: The Investigation of Lexical Semantic Representation in Alzheimer's Disease." Brain and Language 54, no. 2 (August 1996): 179–83. http://dx.doi.org/10.1006/brln.1996.0069.

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Mohamadzadeh, Sajad, Sadegh Pasban, Javad Zeraatkar-Moghadam, and Amir Keivan Shafiei. "Parkinson’s Disease Detection by Using Feature Selection and Sparse Representation." Journal of Medical and Biological Engineering 41, no. 4 (June 18, 2021): 412–21. http://dx.doi.org/10.1007/s40846-021-00626-y.

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Berenyi, Freya, Sara Booth, Anna Spathis, and Natasha Smallwood. "Negative visual representation of chronic obstructive pulmonary disease occurs online." ERJ Open Research 6, no. 4 (October 2020): 00549–2020. http://dx.doi.org/10.1183/23120541.00549-2020.

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Journal articles on the topic 'Disease representation'

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