Literatura académica sobre el tema "Clinical annotations"
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Artículos de revistas sobre el tema "Clinical annotations"
Yost, Shawn, Márton Münz, Shazia Mahamdallie, Anthony Renwick, Elise Ruark y Nazneen Rahman. "Clinical Annotation Reference Templates: a resource for consistent variant annotation". Wellcome Open Research 3 (14 de noviembre de 2018): 146. http://dx.doi.org/10.12688/wellcomeopenres.14924.1.
Texto completoAnderson, Matthew, Salman Sadiq, Muzammil Nahaboo Solim, Hannah Barker, David H. Steel, Maged Habib y Boguslaw Obara. "Biomedical Data Annotation: An OCT Imaging Case Study". Journal of Ophthalmology 2023 (22 de agosto de 2023): 1–9. http://dx.doi.org/10.1155/2023/5747010.
Texto completoCronkite, David, Bradley Malin, John Aberdeen, Lynette Hirschman y David Carrell. "Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification". Methods of Information in Medicine 55, n.º 04 (2016): 356–64. http://dx.doi.org/10.3414/me15-01-0122.
Texto completoPark, Jimyung, Seng Chan You, Eugene Jeong, Chunhua Weng, Dongsu Park, Jin Roh, Dong Yun Lee et al. "A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study". JMIR Medical Informatics 9, n.º 3 (30 de marzo de 2021): e23983. http://dx.doi.org/10.2196/23983.
Texto completoYssel, Anna E. J., Shu-Min Kao, Yves Van de Peer y Lieven Sterck. "ORCAE-AOCC: A Centralized Portal for the Annotation of African Orphan Crop Genomes". Genes 10, n.º 12 (20 de noviembre de 2019): 950. http://dx.doi.org/10.3390/genes10120950.
Texto completoKeegan, Niamh M., Samantha E. Vasselman, Ethan Barnett, Barbara Nweji, Emily Carbone, Alexander Blum, Michael J. Morris et al. "Clinical annotations for prostate cancer research: Defining data elements, creating a reproducible analytical pipeline, and assessing data quality." Journal of Clinical Oncology 40, n.º 6_suppl (20 de febrero de 2022): 64. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.064.
Texto completoMoore, Jill E., Xiao-Ou Zhang, Shaimae I. Elhajjajy, Kaili Fan, Henry E. Pratt, Fairlie Reese, Ali Mortazavi y Zhiping Weng. "Integration of high-resolution promoter profiling assays reveals novel, cell type–specific transcription start sites across 115 human cell and tissue types". Genome Research 32, n.º 2 (23 de diciembre de 2021): 389–402. http://dx.doi.org/10.1101/gr.275723.121.
Texto completode Bruijn, Ino, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson et al. "Abstract 1156: Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer". Cancer Research 82, n.º 12_Supplement (15 de junio de 2022): 1156. http://dx.doi.org/10.1158/1538-7445.am2022-1156.
Texto completoQueirós, Pedro, Polina Novikova, Paul Wilmes y Patrick May. "Unification of functional annotation descriptions using text mining". Biological Chemistry 402, n.º 8 (13 de mayo de 2021): 983–90. http://dx.doi.org/10.1515/hsz-2021-0125.
Texto completoBax, Martin, Hilary Hart y Sue Jenkins. "Annotations". Developmental Medicine & Child Neurology 23, n.º 1 (12 de noviembre de 2008): 92–95. http://dx.doi.org/10.1111/j.1469-8749.1981.tb08450.x.
Texto completoTesis sobre el tema "Clinical annotations"
Agarwal, Navneet. "Autοmated depressiοn level estimatiοn : a study οn discοurse structure, input representatiοn and clinical reliability". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC215.
Texto completoGiven the severe and widespread impact of depression, significant research initiatives have been undertaken to define systems for automated depression assessment. The research presented in this dissertation revolves around the following questions that remain relatively unexplored despite their relevance within automated depression assessment domain; (1) the role of discourse structure in mental health analysis, (2) the relevance of input representation towards the predictive abilities of neural network models, and (3) the importance of domain expertise in automated depression detection.The dyadic nature of patient-therapist interviews ensures the presence of a complex underlying structure within the discourse. Within this thesis, we first establish the importance of therapist questions within the neural network model's input, before showing that a sequential combination of patient and therapist input is a sub-optimal strategy. Consequently, Multi-view architectures are proposed as a means of incorporating the discourse structure within the learning process of neural networks. Experimental results with two different text encodings show the advantages of the proposed multi-view architectures, validating the relevance of retaining discourse structure within the model's training process.Having established the need to retain the discourse structure within the learning process, we further explore graph based text representations. The research conducted in this context highlights the impact of input representations not only in defining the learning abilities of the model, but also in understanding their predictive process. Sentence Similarity Graphs and Keyword Correlation Graphs are used to exemplify the ability of graphical representations to provide varying perspectives of the same input, highlighting information that can not only improve the predictive performance of the models but can also be relevant for medical professionals. Multi-view concept is also incorporated within the two graph structures to further highlight the difference in the perspectives of the patient and the therapist within the same interview. Furthermore, it is shown that visualization of the proposed graph structures can provide valuable insights indicative of subtle changes in patient and therapist's behavior, hinting towards the mental state of the patient.Finally, we highlight the lack of involvement of medical professionals within the context of automated depression detection based on clinical interviews. As part of this thesis, clinical annotations of the DAIC-WOZ dataset were performed to provide a resource for conducting interdisciplinary research in this field. Experiments are defined to study the integration of the clinical annotations within the neural network models applied to symptom-level prediction task within the automated depression detection domain. Furthermore, the proposed models are analyzed in the context of the clinical annotations to analogize their predictive process and psychological tendencies with those of medical professionals, a step towards establishing them as reliable clinical tools
Weng, Chunhua. "Supporting collaborative clinical trial protocol writing through an annotation design /". Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/7155.
Texto completoVelupillai, Sumithra. "Shades of Certainty : Annotation and Classification of Swedish Medical Records". Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-74828.
Texto completoKholghi, Mahnoosh. "Active learning for concept extraction from clinical free text". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/112420/1/Mahnoosh_Kholghi_Thesis.pdf.
Texto completoHallier, Andrea Rae. "Variant-curation and database instantiation (Variant-CADI): an integrated software system for the automation of collection, annotation and management of variations in clinical genetic testing". Thesis, University of Iowa, 2016. https://ir.uiowa.edu/etd/2218.
Texto completoGhous, Hamid. "Building a robust clinical diagnosis support system for childhood cancer using data mining methods". Thesis, 2016. http://hdl.handle.net/10453/90061.
Texto completoProgress in understanding core pathways and processes of cancer requires thorough analysis of many coding and noncoding regions of the genome. Data mining and knowledge discovery have been applied to datasets across many industries, including bioinformatics. However, data mining faces a major challenge in its application to bioinformatics: the diversity and dimensionality of biomedical data. The term ‘big data’ was applied to the clinical domain by Yoo et al. (2014), specifically referring to single nucleotide polymorphism (SNP) and gene expression data. This research thesis focuses on three different types of data: gene-annotations, gene expression and single nucleotide polymorphisms. Genetic association studies have led to the discovery of single genetic variants associated with common diseases. However, complex diseases are not caused by a single gene acting alone but are the result of complex linear and non-linear interactions among different types of microarray data. In this scenario, a single gene can have a small effect on disease but cannot be the major cause of the disease. For this reason there is a critical need to implement new approaches which take into account linear and non-linear gene-gene and patient-patient interactions that can eventually help in diagnosis and prognosis of complex diseases. Several computational methods have been developed to deal with gene annotations, gene expressions and SNP data of complex diseases. However, analysis of every gene expression and SNP profile, and finding gene-to-gene relationships, is computationally infeasible because of the high-dimensionality of data. In addition, many computational methods have problems with scaling to large datasets, and with overfitting. Therefore, there is growing interest in applying data mining and machine learning approaches to understand different types of microarray data. Cancer is the disease that kills the most children in Australia (Torre et al., 2015). Within this thesis, the focus is on childhood Acute Lymphoblastic Leukaemia. Acute Lymphoblastic Leukaemia is the most common childhood malignancy with 24% of all new cancers occurring in children within Australia (Coates et al., 2001). According to the American Cancer Society (2016), a total of 6,590 cases of ALL have been diagnosed across all age groups in USA and the expected deaths are 1,430 in 2016. The project uses different data mining and visualisation methods applied on different types of biological data: gene annotations, gene expression and SNPs. This thesis focuses on three main issues in genomic and transcriptomic data studies: (i) Proposing, implementing and evaluating a novel framework to find functional relationships between genes from gene-annotation data. (ii) Identifying an optimal dimensionality reduction method to classify between relapsed and non-relapsed ALL patients using gene expression. (iii) Proposing, implementing and evaluating a novel feature selection approach to identify related metabolic pathways in ALL This thesis proposes, implements and validates an efficient framework to find functional relationships between genes based on gene-annotation data. The framework is built on a binary matrix and a proximity matrix, where the binary matrix contains information related to genes and their functionality, while the proximity matrix shows similarity between different features. The framework retrieves gene functionality information from Gene Ontology (GO), a publicly available database, and visualises the functional related genes using singular value decomposition (SVD). From a simple list of gene-annotations, this thesis retrieves features (i.e Gene Ontology terms) related to each gene and calculates a similarity measure based on the distance between terms in the GO hierarchy. The distance measures are based on hierarchical structure of Gene Ontology and these distance measures are called similarity measures. In this framework, two different similarity measures are applied: (i) A hop-based similarity measure where the distance is calculated based on the number of links between two terms. (ii) An information-content similarity measure where the similarity between terms is based on the probability of GO terms in the gene dataset. This framework also identifies which method performs better among these two similarity measures at identifying functional relationships between genes. Singular value decomposition method is used for visualisation, having the advantage that multiple types of relationships can be visualised simultaneously (gene-to-gene, term-to-term and gene-to-term) In this thesis a novel framework is developed for visualizing patient-to-patient relationships using gene expression values. The framework builds on the random forest feature selection method to filter gene expression values and then applies different linear and non-linear machine learning methods to them. The methods used in this framework are Principal Component Analysis (PCA), Kernel Principal Component Analysis (kPCA), Local Linear Embedding (LLE), Stochastic Neighbour Embedding (SNE) and Diffusion Maps. The framework compares these different machine learning methods by tuning different parameters to find the optimal method among them. Area under the curve (AUC) is used to rank the results and SVM is used to classify between relapsed and non-relapsed patients. The final section of the thesis proposes, implements and validates a framework to find active metabolic pathways in ALL using single nucleotide polymorphism (SNP) profiles. The framework is based on the random forest feature selection method. A collected dataset of ALL patient and healthy controls is constructed and later random forest is applied using different parameters to find highly-ranked SNPs. The credibility of the model is assessed based on the error rate of the confusion matrix and kappa values. Selected high ranked SNPs are used to retrieve metabolic pathways related to ALL from the KEGG metabolic pathways database. The methodologies and approaches presented in this thesis emphasise the critical role that different types of microarray data play in understanding complex diseases like ALL. The availability of flexible frameworks for the task of disease diagnosis and prognosis, as proposed in this thesis, will play an important role in understanding the genetic basis to common complex diseases. This thesis contributes to knowledge in two ways: (i) Providing novel data mining and visualisation frameworks to handle biological data. (ii) Providing novel visualisations for microarray data to increase understanding of disease.
"Genome annotation and identification of blood invasiveness genetic determinants in Salmonella Typhimurium clinical isolates from Hong Kong". 2013. http://library.cuhk.edu.hk/record=b5549745.
Texto completo本研究同時對本地的血液和糞便分離菌株，連同國外的臨床分離菌株，進行了廣泛的比對，用以識別全身性沙門氏菌感染的潛在遺傳因素。 本研究進行了以下基因分析：（1）多位點序列分型（MLST）；（2）在小鼠全身性感染中涉及的主調控因子和關鍵元素; 及（3）人類腸胃道感染中涉及的基因。然而，這些分析產生只能對全身性沙門氏菌感染提供有限的見解。然而，透過使用RAST註釋系統，我們於其中三個血液分離菌株中發現了一個的額外的螯鐵蛋白aerobactin鐵採集系統。儘管在體外實驗中，這些血液分離菌株並沒有明顯的生長優勢，但實驗結果表明，在缺乏鐵的培養液中，aerobactin基因的表達水平是比較高的。此外，我們亦於其中四個血液分離菌株中，發現負責細胞色素c熟成(ccm)的基因座均被中斷。這可能改變了這些血液分離菌株中細胞色素c的生物合成途徑。這些鐵採集和同化機制的觀察均為未來全身性沙門氏菌感染的研究提供了可能的發展方向。
本研究同時識別了用以分別本地及海外的沙門氏鼠傷寒菌菌株的分子標記，並在鮭魚和生菜的接種實驗中，展現了它們分辨本地及海外菌株的能力。然而，在投入實際應用之前，這些標記尚需要進一步的驗證和測試，以便確定快速檢測方法的有效性。
Foodborne infection is a common but important public health issue worldwide. Salmonella enterica serovar Typhimurium is frequently isolated from outbreaks as one of the common bacterial causative agents. Following the availability of the genome sequence of the reference lab strain LT2 in 2001, nine genomes of S. Typhimurium had been sequenced since then. Recently, genomes of ten local S. Typhimurium clinical isolates have been assembled in our laboratory. In order to provide high quality annotation of these genome sequences, the predicted gene sets were submitted to the quality control tool GenePRIMP (Gene PRediction IMprovement Pipeline) to identify potentially erroneous and abnormal gene calls. The GenePRIMP reports for the local blood isolate 78896 and stool isolate 1047518 were manually inspected and more than 270 genes were amended individually for each isolate. Functional annotation had also been performed for the 10 local isolates. Genomic and virulent elements including Salmonella Pathogenicity Islands (SPIs), virulence factors, tRNAs and small non-coding RNAs, prophage elements and CRISPRs structures had been annotated. The KEGG pathways provided a further means of functional annotation.
The local blood and stool isolates, together with the sequenced foreign clinical isolates, had also been extensively compared to identify potential genetic determinants of Salmonella systemic infection. (1) Multilocus sequence typing (MLST); (2) Alignment of master regulators and key players of systemic infection in mice; and (3) Analyses of the genes responsible for human gastrointestinal tract infection had been performed. However, these analyses yielded limited insights on systemic infection. Alternatively, using subsystems annotation by RAST, an additional aerobactin siderophore iron acquisition system was shown to be prevalent among three of the blood isolates. Despite no obvious growth advantage was offered to the blood isolates in an in vitro experiment, it was demonstrated that expression of the aerobactin genes was higher in iron-depleted culturing medium. In addition, a disrupted cytochrome c maturation (ccm) locus that may alter the cytochrome c biogenesis pathway was also identified in four of the blood isolates. These observations in iron acquisition and assimilation mechanisms suggest their potential in future direction of Salmonella systemic infection studies.
Molecular markers specific to local and foreign S. Typhimurium isolates were also identified and their utility in differentiating local and foreign isolates was demonstrated in a pilot spiking experiment using raw salmon and lettuce. These markers will require further verification and testing prior to actual application in real-world settings in order to examine the validity of the rapid detection method.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Detailed summary in vernacular field only.
Cheng, Chi Keung.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 124-146).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Abstract of thesis entitled --- p.iii
摘要 --- p.v
Acknowledgements --- p.vii
Table of Contents --- p.viii
List of Tables --- p.xi
List of Figures --- p.xiii
Abbreviations --- p.xiv
Chapter Chapter 1 --- Literature Review --- p.1
Chapter 1.1 --- Introduction and Taxonomy --- p.1
Chapter 1.2 --- Epidemiology of Salmonella Typhimurium infections --- p.2
Chapter 1.3 --- Pathogenesis of Salmonella Typhimurium infection --- p.4
Chapter 1.3.1 --- Infection mechanisms --- p.4
Chapter 1.3.2 --- Salmonella Pathogenicity Islands --- p.6
Chapter 1.3.3 --- Regulation of virulence --- p.9
Chapter 1.4 --- Non-typhoid Salmonella (NTS) systemic infection --- p.11
Chapter 1.4.1 --- Epidemiology of NTS systemic infection --- p.11
Chapter 1.4.2 --- Salmonella Typhimurium multidrug resistance --- p.12
Chapter 1.5 --- Salmonella Typhimurium genomics --- p.15
Chapter 1.5.1 --- Salmonella Typhimurium genome sequencing --- p.15
Chapter 1.5.2 --- Comparative studies on Salmonella genomes --- p.17
Chapter 1.6 --- Aims of project --- p.19
Chapter Chapter 2 --- Curation and detailed annotation of genomes of local Salmonella Typhimurium clinical isolates --- p.22
Chapter 2.1 --- Introduction --- p.22
Chapter 2.2 --- Materials and Methods --- p.27
Chapter 2.2.1 --- Manual curation of GenePRIMP results --- p.27
Chapter 2.2.2 --- Salmonella Pathogenicity Islands (SPIs) and virulence factors annotation --- p.29
Chapter 2.2.3 --- Small RNA and t-RNA annotation --- p.29
Chapter 2.2.4 --- Phage elements annotation --- p.30
Chapter 2.2.5 --- CRISPRs annotation --- p.30
Chapter 2.2.6 --- KEGG annotation --- p.30
Chapter 2.3 --- Results --- p.32
Chapter 2.3.1 --- Manual curation of GenePRIMP results --- p.32
Chapter 2.3.1.1 --- Short genes --- p.35
Chapter 2.3.1.2 --- Long genes --- p.35
Chapter 2.3.1.3 --- Unique genes --- p.36
Chapter 2.3.1.4 --- Overlapped genes --- p.36
Chapter 2.3.1.5 --- Broken genes --- p.37
Chapter 2.3.2 --- Salmonella Pathogenicity Islands (SPIs) and virulence factors annotation --- p.37
Chapter 2.3.2.1 --- Salmonella Pathogenicity Islands (SPIs) annotation --- p.37
Chapter 2.3.2.2 --- Virulence factors annotation --- p.44
Chapter 2.3.3 --- Small RNA and t-RNA annotation --- p.44
Chapter 2.3.4 --- Phage elements annotation --- p.44
Chapter 2.3.5 --- CRISPRs annotation --- p.50
Chapter 2.3.6 --- KEGG annotation --- p.51
Chapter 2.4 --- Discussion --- p.53
Chapter 2.4.1 --- Manual curation of GenePRIMP results --- p.53
Chapter 2.4.1.1 --- Gene amendment not required --- p.54
Chapter 2.4.1.2 --- Genes with boundaries relocated --- p.54
Chapter 2.4.1.3 --- Genes to be discarded --- p.55
Chapter 2.4.1.4 --- Gene pairs to be fused --- p.55
Chapter 2.4.1.5 --- Potential pseudogenes formation --- p.56
Chapter 2.4.2 --- Salmonella Pathogenicity Islands (SPIs) annotation --- p.57
Chapter 2.4.3 --- Virulence factors annotation --- p.57
Chapter 2.4.4 --- Small RNA and t-RNA annotation --- p.58
Chapter 2.4.5 --- Phage elements annotation --- p.59
Chapter Chapter 3 --- Identification of genetic determinants of blood invasiveness in local S. Typhimurium clinical isolates --- p.61
Chapter 3.1 --- Introduction --- p.61
Chapter 3.2 --- Materials and Methods --- p.66
Chapter 3.2.1 --- Multilocus Sequence Typing (MLST) --- p.66
Chapter 3.2.2 --- Phage elements annotation for foreign isolates --- p.67
Chapter 3.2.3 --- Alignment of genes inferred to play important roles in NTS systemic --- p.infection67
Chapter 3.2.4 --- Alignment of genes inferred to involved during infection in the gastrointestinal (GI) tract --- p.68
Chapter 3.2.5 --- Subsystems assignment using Rapid Annotation using Subsystem Technology (RAST) server --- p.68
Chapter 3.2.6 --- Growth analysis of local S. Typhimurium clinical isolates in iron-limiting environment --- p.69
Chapter 3.2.7 --- Reverse transcription and real-time PCR --- p.70
Chapter 3.2.7.1 --- Primer design and verification --- p.70
Chapter 3.2.7.2 --- cDNA synthesis and real-time PCR --- p.70
Chapter 3.3 --- Results --- p.73
Chapter 3.3.1 --- Multilocus Sequence Typing (MLST) --- p.73
Chapter 3.3.2 --- Phage elements annotation for foreign isolates --- p.73
Chapter 3.3.3 --- Alignment of genes inferred to play important roles in NTS systemic infection --- p.74
Chapter 3.3.4 --- Alignment of genes inferred to involved during infection in the gastrointestinal (GI) tract --- p.79
Chapter 3.3.4.1 --- Acid tolerance response --- p.79
Chapter 3.3.4.2 --- Epithelial cells attachment --- p.80
Chapter 3.3.4.3 --- Epithelial cells invasion --- p.83
Chapter 3.3.4.4 --- Survival within macrophages --- p.83
Chapter 3.3.5 --- RAST subsystem analysis --- p.86
Chapter 3.3.6 --- Growth analysis and aerobactin genes expression --- p.87
Chapter 3.4 --- Discussion --- p.93
Chapter Chapter 4 --- Molecular markers identification and testing on selected foodstuff for local S. Typhimurium isolates --- p.97
Chapter 4.1 --- Introduction --- p.97
Chapter 4.2 --- Materials and Methods --- p.101
Chapter 4.2.1 --- Molecular markers identification --- p.101
Chapter 4.2.2 --- Primer design and verification --- p.101
Chapter 4.2.3 --- Spiking experiments on selected food samples --- p.103
Chapter 4.2.4 --- Quantitative TaqMan real-time PCR --- p.103
Chapter 4.3 --- Results --- p.105
Chapter 4.3.1 --- Molecular markers identification --- p.105
Chapter 4.3.2 --- Spiking experiments and TaqMan real-time PCR --- p.109
Chapter 4.4 --- Discussion --- p.113
Chapter 4.4.1 --- Molecular markers identification --- p.113
Chapter 4.4.2 --- Spiking experiments and TaqMan real-time PCR --- p.114
Chapter Chapter 5 --- General discussion --- p.116
Chapter 5.1 --- Manual curation of GenePRIMP results --- p.116
Chapter 5.2 --- Functional annotation of local S. Typhimurium genomes --- p.118
Chapter 5.3 --- Systemic infection studies --- p.120
Chapter 5.4 --- Molecular markers identification and spiking experiments --- p.121
Chapter 5.5 --- Conclusion and future perspectives --- p.122
References --- p.124
Libros sobre el tema "Clinical annotations"
American Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, Ill: AMA Press, 2004.
Buscar texto completoAmerican Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, Ill: AMA Press, 2006.
Buscar texto completoAmerican Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, IL: The Association, 2000.
Buscar texto completoAmerican Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. Chicago, IL: The Association, 1998.
Buscar texto completoOliver J. Bear Don't Walk. Inferring Race and Ethnicity from Clinical Notes: Annotation, Model Auditing, and Ethical Implications. [New York, N.Y.?]: [publisher not identified], 2022.
Buscar texto completoBillroth, Christian Albert Theodor. Clinical Surgery, Extracts From the Reports of Surgical Practice, 1860-1876, Tr. and Ed., With Annotations, by C.T. Dent. Arkose Press, 2015.
Buscar texto completoAffa, AMA Council on Ethical and Judicial y American Medical Association. Code of Medical Ethics 2004-2005: Current Opinions with Annotations (Code of Medical Ethics). American Medical Association Press, 2004.
Buscar texto completoClinical Surgery. Extracts From the Reports of Surgical Practice Between the Years 1860-1876. Translated From the Original, and Edited, With Annotations, by C. T. Dent. Franklin Classics, 2018.
Buscar texto completoClinical Surgery. Extracts from the Reports of Surgical Practice Between the Years 1860-1876. Translated from the Original, and Edited, with Annotations, by C. T. Dent. Creative Media Partners, LLC, 2023.
Buscar texto completoBillroth, Theodor. Clinical Surgery. Extracts from the Reports of Surgical Practice Between the Years 1860-1876. Translated from the Original, and Edited, with Annotations, by C. T. Dent. Franklin Classics Trade Press, 2018.
Buscar texto completoCapítulos de libros sobre el tema "Clinical annotations"
Rahman, Fahrurrozi y Juliana Bowles. "Semantic Annotations in Clinical Guidelines". En From Data to Models and Back, 190–205. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70650-0_12.
Texto completoDel Rio, Mauro, Luca Lianas, Oskar Aspegren, Giovanni Busonera, Francesco Versaci, Renata Zelic, Per H. Vincent et al. "AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies". En Lecture Notes in Computer Science, 545–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_48.
Texto completoPetit, Olivier, Nicolas Thome, Arnaud Charnoz, Alexandre Hostettler y Luc Soler. "Handling Missing Annotations for Semantic Segmentation with Deep ConvNets". En Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 20–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_3.
Texto completoPłaczek, Aleksander, Alicja Płuciennik, Mirosław Pach, Michał Jarząb y Dariusz Mrozek. "The Role of Feature Selection in Text Mining in the Process of Discovering Missing Clinical Annotations – Case Study". En Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis, 248–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19093-4_19.
Texto completoCai, Jinzheng, Youbao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang y Ronald M. Summers. "Accurate Weakly-Supervised Deep Lesion Segmentation Using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST". En Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 396–404. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00937-3_46.
Texto completoSavova, Guergana, Sameer Pradhan, Martha Palmer, Will Styler, Wendy Chapman y Noémie Elhadad. "Annotating the Clinical Text – MiPACQ, ShARe, SHARPn and THYME Corpora". En Handbook of Linguistic Annotation, 1357–78. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_52.
Texto completoKuroda, Makoto y Keiichi Hiramatsu. "Genome Sequencing and Annotation". En Genomics, Proteomics, and Clinical Bacteriology, 29–45. Totowa, NJ: Humana Press, 2004. http://dx.doi.org/10.1385/1-59259-763-7:029.
Texto completoTinhofer, Ingeborg, Ulrich Keilholz y Damian Rieke. "How to Standardize Molecular Profiling Programs for Routine Patient Care". En Critical Issues in Head and Neck Oncology, 37–49. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23175-9_4.
Texto completoNajafabadipour, Marjan, Juan Manuel Tuñas, Alejandro Rodríguez-González y Ernestina Menasalvas. "Lung Cancer Concept Annotation from Spanish Clinical Narratives". En Lecture Notes in Computer Science, 153–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-06016-9_15.
Texto completoTao, Yimo, Zhigang Peng, Bing Jian, Jianhua Xuan, Arun Krishnan y Xiang Sean Zhou. "Robust Learning-Based Annotation of Medical Radiographs". En Medical Content-Based Retrieval for Clinical Decision Support, 77–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11769-5_8.
Texto completoActas de conferencias sobre el tema "Clinical annotations"
Velupillai, Sumithra. "Semantic annotations in clinical documentation". En the third workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871962.1871968.
Texto completoVizza, Patrizia, Giuseppe Tradigo, Elvis Kallaverja, Maria Giulia Cristofaro, Giuseppe Lucio Cascini y Pierangelo Veltri. "Annotations for clinical data enrichment". En 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669480.
Texto completoRobinson, Gain, Lin Li, Yimin Zhu, Rachel DuPont, Laura Engstrom, Laxminarayan G. Hegde, Jie Zhang-Hoover, Kimberly Bettano y Antong Chen. "Deep learning-based automated lung tumor segmentation in mouse preclinical micro-CT scans with limited annotations". En Clinical and Biomedical Imaging, editado por Barjor S. Gimi y Andrzej Krol. SPIE, 2024. http://dx.doi.org/10.1117/12.3006521.
Texto completoGalveia, Jose N., Antonio Travassos y Luis A. da Silva Cruz. "An Ophthalmology Clinical Decision Support System Based on Clinical Annotations, Ontologies and Images". En 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2018. http://dx.doi.org/10.1109/cbms.2018.00024.
Texto completoLlorca, Ignacio, Florian Borchert y Matthieu-P. Schapranow. "A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation". En Proceedings of the 5th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.23.
Texto completoRoy, Sharmili, Michael S. Brown y George L. Shih. "Extracting volumetric information from standard two-dimensional radiological annotations within the clinical workflow". En 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2012. http://dx.doi.org/10.1109/bibmw.2012.6470226.
Texto completoSchäfer, Henning, Ahmad Idrissi-Yaghir, Peter Horn y Christoph Friedrich. "Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language". En Proceedings of the 4th Clinical Natural Language Processing Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.clinicalnlp-1.6.
Texto completoJiang, Zhengqiang, Phuong D. Trieu, Ziba Gandomkar, Seyedamir Tavakoli Taba, Melissa L. Barron y Sarah J. Lewis. "How do you solve a problem like concordance? A study of radiologists’ clinical annotations for mammographic AI training". En Image Perception, Observer Performance, and Technology Assessment, editado por Yan Chen y Claudia R. Mello-Thoms. SPIE, 2023. http://dx.doi.org/10.1117/12.2654147.
Texto completoZhai, Haijun, Todd Lingren, Louise Deleger, Qi Li, Megan Kaiser, Laura Stoutenborough y Imre Solti. "Cheap, Fast, and Good Enough for the Non-biomedical Domain but is It Usable for Clinical Natural Language Processing? Evaluating Crowdsourcing for Clinical Trial Announcement Named Entity Annotations". En 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). IEEE, 2012. http://dx.doi.org/10.1109/hisb.2012.31.
Texto completoLee, Min Hun, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino y Sergi Bermúdez i Badia. "Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System: A Case Study on Physical Stroke Rehabilitation Assessment". En IUI '22: 27th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490099.3511112.
Texto completoInformes sobre el tema "Clinical annotations"
Xu, Chao, Walter Forkel, Stefan Borgwardt, Franz Baader y Beihai Zhou. Automatic Translation of Clinical Trial Eligibility Criteria into Formal Queries. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.224.
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