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Artigos de revistas sobre o assunto "Clinical annotations"

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Yost, Shawn, Márton Münz, Shazia Mahamdallie, Anthony Renwick, Elise Ruark e Nazneen Rahman. "Clinical Annotation Reference Templates: a resource for consistent variant annotation". Wellcome Open Research 3 (14 de novembro de 2018): 146. http://dx.doi.org/10.12688/wellcomeopenres.14924.1.

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Annotating the impact of a variant on a gene is a vital component of genetic medicine and genetic research. Different gene annotations for the same genomic variant are possible, because different structures and sequences for the same gene are available. The clinical community typically use RefSeq NMs to annotate gene variation, which do not always match the reference genome. The scientific community typically use Ensembl ENSTs to annotate gene variation. These match the reference genome, but often do not match the equivalent NM. Often the transcripts used to annotate gene variation are not provided, impeding interoperability and consistency. Here we introduce the concept of the Clinical Annotation Reference Template (CART). CARTs are analogous to the reference genome; they provide a universal standard template so reference genomic coordinates are consistently annotated at the protein level. Naturally, there are many situations where annotations using a specific transcript, or multiple transcripts are useful. The aim of the CARTs is not to impede this practice. Rather, the CART annotation serves as an anchor to ensure interoperability between different annotation systems and variant frequency accuracy. Annotations using other explicitly-named transcripts should also be provided, wherever useful. We have integrated transcript data to generate CARTs for over 18,000 genes, for both GRCh37 and GRCh38, based on the associated NM and ENST identified through the CART selection process. Each CART has a unique ID and can be used individually or as a stable set of templates; CART37A for GRCh37 and CART38A for GRCh38. We have made the CARTs available on the UCSC browser and in different file formats on the Open Science Framework: https://osf.io/tcvbq/. We have also made the CARTtools software we used to generate the CARTs available on GitHub. We hope the CARTs will be useful in helping to drive transparent, stable, consistent, interoperable variant annotation.
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Anderson, Matthew, Salman Sadiq, Muzammil Nahaboo Solim, Hannah Barker, David H. Steel, Maged Habib e 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.

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In ophthalmology, optical coherence tomography (OCT) is a widely used imaging modality, allowing visualisation of the structures of the eye with objective and quantitative cross-sectional three-dimensional (3D) volumetric scans. Due to the quantity of data generated from OCT scans and the time taken for an ophthalmologist to inspect for various disease pathology features, automated image analysis in the form of deep neural networks has seen success for the classification and segmentation of OCT layers and quantification of features. However, existing high-performance deep learning approaches rely on huge training datasets with high-quality annotations, which are challenging to obtain in many clinical applications. The collection of annotations from less experienced clinicians has the potential to alleviate time constraints from more senior clinicians, allowing faster data collection of medical image annotations; however, with less experience, there is the possibility of reduced annotation quality. In this study, we evaluate the quality of diabetic macular edema (DME) intraretinal fluid (IRF) biomarker image annotations on OCT B-scans from five clinicians with a range of experience. We also assess the effectiveness of annotating across multiple sessions following a training session led by an expert clinician. Our investigation shows a notable variance in annotation performance, with a correlation that depends on the clinician’s experience with OCT image interpretation of DME, and that having multiple annotation sessions has a limited effect on the annotation quality.
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Cronkite, David, Bradley Malin, John Aberdeen, Lynette Hirschman e 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.

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SummaryBackground: Clinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized.Objectives: This study models the costs and benefits of incorporating increasing numbers of independent human annotators to identify the instances of PII in a corpus. We used a corpus with gold standard annotations to evaluate the performance of teams of annotators of increasing size.Methods: Four annotators independently identified PII in a 100-document corpus consisting of randomly selected clinical notes from Family Practice clinics in a large integrated health care system. These annotations were pooled and validated to generate a gold standard corpus for evaluation.Results: Recall rates for all PII types ranged from 0.90 to 0.98 for individual annotators to 0.998 to 1.0 for teams of three, when measured against the gold standard. Median cost per PII instance discovered during corpus annotation ranged from $ 0.71 for an individual annotator to $ 377 for annotations discovered only by a fourth annotator.Conclusions: Incorporating a second annotator into a PII annotation process reduces unredacted PII and improves the quality of annotations to 0.99 recall, yielding clear benefit at reasonable cost; the cost advantages of annotation teams larger than two diminish rapidly.
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Park, 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 março de 2021): e23983. http://dx.doi.org/10.2196/23983.

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Background Although electronic health records (EHRs) have been widely used in secondary assessments, clinical documents are relatively less utilized owing to the lack of standardized clinical text frameworks across different institutions. Objective This study aimed to develop a framework for processing unstructured clinical documents of EHRs and integration with standardized structured data. Methods We developed a framework known as Staged Optimization of Curation, Regularization, and Annotation of clinical text (SOCRATex). SOCRATex has the following four aspects: (1) extracting clinical notes for the target population and preprocessing the data, (2) defining the annotation schema with a hierarchical structure, (3) performing document-level hierarchical annotation using the annotation schema, and (4) indexing annotations for a search engine system. To test the usability of the proposed framework, proof-of-concept studies were performed on EHRs. We defined three distinctive patient groups and extracted their clinical documents (ie, pathology reports, radiology reports, and admission notes). The documents were annotated and integrated into the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) database. The annotations were used for creating Cox proportional hazard models with different settings of clinical analyses to measure (1) all-cause mortality, (2) thyroid cancer recurrence, and (3) 30-day hospital readmission. Results Overall, 1055 clinical documents of 953 patients were extracted and annotated using the defined annotation schemas. The generated annotations were indexed into an unstructured textual data repository. Using the annotations of pathology reports, we identified that node metastasis and lymphovascular tumor invasion were associated with all-cause mortality among colon and rectum cancer patients (both P=.02). The other analyses involving measuring thyroid cancer recurrence using radiology reports and 30-day hospital readmission using admission notes in depressive disorder patients also showed results consistent with previous findings. Conclusions We propose a framework for hierarchical annotation of textual data and integration into a standardized OMOP-CDM medical database. The proof-of-concept studies demonstrated that our framework can effectively process and integrate diverse clinical documents with standardized structured data for clinical research.
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Yssel, Anna E. J., Shu-Min Kao, Yves Van de Peer e Lieven Sterck. "ORCAE-AOCC: A Centralized Portal for the Annotation of African Orphan Crop Genomes". Genes 10, n.º 12 (20 de novembro de 2019): 950. http://dx.doi.org/10.3390/genes10120950.

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ORCAE (Online Resource for Community Annotation of Eukaryotes) is a public genome annotation curation resource. ORCAE-AOCC is a branch that is dedicated to the genomes published as part of the African Orphan Crops Consortium (AOCC). The motivation behind the development of the ORCAE platform was to create a knowledge-based website where the research-community can make contributions to improve genome annotations. All changes to any given gene-model or gene description are stored, and the entire annotation history can be retrieved. Genomes can either be set to “public” or “restricted” mode; anonymous users can browse public genomes but cannot make any changes. Aside from providing a user- friendly interface to view genome annotations, the platform also includes tools and information (such as gene expression evidence) that enables authorized users to edit and validate genome annotations. The ORCAE-AOCC platform will enable various stakeholders from around the world to coordinate their efforts to annotate and study underutilized crops.
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Keegan, 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 fevereiro de 2022): 64. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.064.

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64 Background: Routine clinical data from the electronic medical record are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. We sought to develop a prostate cancer-specific database with a defined source hierarchy for clinical annotations and to evaluate data reproducibility. Methods: At a comprehensive cancer center, we designed and implemented a clinical database for men with prostate cancer and clinical-grade paired tumor–normal sequencing for whom we performed team-based retrospective clinical data annotation from the electronic medical record, using a prostate cancer-specific data dictionary. We developed an open-source R package for data processing. We then evaluated completeness of data elements, reproducibility of team-based annotation using blinded repeat annotation by a medical oncologist as the reference, and the impact of measurement error on bias in survival analyses. Results: Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2,261 patients and their 2,631 genomically profiled samples. Completeness of data elements was generally high, between 55% to 99% for elements of clinical TNM staging, self-reported race, biopsy Gleason score, and presence of variant histologies, both for the team-based annotation and the repeat annotation. Comparing team-based annotation to the repeat annotation (100 patients/samples), reproducibility of annotations was high to very high. For 7 binary data elements, both sensitivity and specificity of the team-based annotation reached or exceeded 90%. The T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest. Conclusions: With a prostate cancer-specific data dictionary and quality control measures, manual team-based annotations can be scalable and reproducible. The data dictionary and the R package for reproducible data processing tools provided (https://stopsack.github.io/prostateredcap) are freely available to help increase data quality in clinical prostate cancer research.
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Moore, Jill E., Xiao-Ou Zhang, Shaimae I. Elhajjajy, Kaili Fan, Henry E. Pratt, Fairlie Reese, Ali Mortazavi e 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 dezembro de 2021): 389–402. http://dx.doi.org/10.1101/gr.275723.121.

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Accurate transcription start site (TSS) annotations are essential for understanding transcriptional regulation and its role in human disease. Gene collections such as GENCODE contain annotations for tens of thousands of TSSs, but not all of these annotations are experimentally validated nor do they contain information on cell type–specific usage. Therefore, we sought to generate a collection of experimentally validated TSSs by integrating RNA Annotation and Mapping of Promoters for the Analysis of Gene Expression (RAMPAGE) data from 115 cell and tissue types, which resulted in a collection of approximately 50 thousand representative RAMPAGE peaks. These peaks are primarily proximal to GENCODE-annotated TSSs and are concordant with other transcription assays. Because RAMPAGE uses paired-end reads, we were then able to connect peaks to transcripts by analyzing the genomic positions of the 3′ ends of read mates. Using this paired-end information, we classified the vast majority (37 thousand) of our RAMPAGE peaks as verified TSSs, updating TSS annotations for 20% of GENCODE genes. We also found that these updated TSS annotations are supported by epigenomic and other transcriptomic data sets. To show the utility of this RAMPAGE rPeak collection, we intersected it with the NHGRI/EBI genome-wide association study (GWAS) catalog and identified new candidate GWAS genes. Overall, our work shows the importance of integrating experimental data to further refine TSS annotations and provides a valuable resource for the biological community.
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de 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 junho de 2022): 1156. http://dx.doi.org/10.1158/1538-7445.am2022-1156.

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Abstract Interpreting genomic variants in tumor samples presents a challenge in research and the clinical setting. A major barrier is that information about variants is fragmented across disparate databases, and aggregating information from these requires building extensive infrastructure. To this end, we have developed Genome Nexus, a one stop shop for variant annotation, equipped with a powerful API for bulk annotation of variants and a user friendly interface for cancer researchers. Genome Nexus is available at https://www.genomenexus.org. It a) aggregates variant information from a large number of sources that are relevant to cancer research and clinical applications; b) allows high-performance programmatic access to the aggregated data via a unified API; c) provides a search interface and a reference page for individual cancer variants; d) provides user-friendly tools for annotating variants in patients; e) is freely available under an open source license and can be installed in a private cloud or local environment. Genome Nexus contains annotations from more than a dozen resources, including those that provide variant effect information (VEP), protein sequence annotation (Uniprot, Pfam, dbPTM), functional consequence prediction (Polyphen-2, Mutation Assessor, SIFT), population prevalence (gnomAD, dbSNP, ExAC), cancer population prevalence (Cancer Hotspots, SignalDB) and clinical actionability (OncoKB, CIViC, Clinvar). The annotations can be accessed through the website, the API, and a command line client. Genome Nexus is unique in providing a user friendly interface specific to cancer that allows high performance annotation of any variant. It is the main annotation service for the popular cancer genomics tool cBioPortal, which serves thousands of users daily. It is also offered as a standalone tool for annotation, allowing researchers and clinicians as well as genomic infrastructure developers to leverage it directly in their own workflows. For example, a local installation of Genome Nexus is used for annotating all variants in AACR Project GENIE. Citation Format: Ino de Bruijn, Xiang Li, Onur Sumer, Benjamin Gross, Robert Sheridan, Angelica Ochoa, Manda Wilson, Avery Wang, Hongxin Zhang, Aaron Lisman, Adam Abeshouse, Sander Rodenburg, Sjoerd van Hagen, Remond Fijneman, Gerrit Meijer, Nikolaus Schultz, Jianjiong Gao. Genome Nexus: A comprehensive resource for the annotation and interpretation of genomic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1156.
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Queirós, Pedro, Polina Novikova, Paul Wilmes e Patrick May. "Unification of functional annotation descriptions using text mining". Biological Chemistry 402, n.º 8 (13 de maio de 2021): 983–90. http://dx.doi.org/10.1515/hsz-2021-0125.

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Abstract A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it is possible to identify highly similar functional descriptions, thus strengthening the confidence of the final protein functional annotation and providing a redundancy-free output. Here we present UniFunc, a text mining approach that is able to detect similar functional descriptions with high precision. UniFunc was built as a small module and can be independently used or integrated into protein function annotation pipelines. By removing the need to individually analyse and compare annotation results, UniFunc streamlines the complementary use of multiple reference datasets.
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Bax, Martin, Hilary Hart e Sue Jenkins. "Annotations". Developmental Medicine & Child Neurology 23, n.º 1 (12 de novembro de 2008): 92–95. http://dx.doi.org/10.1111/j.1469-8749.1981.tb08450.x.

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Teses / dissertações sobre o assunto "Clinical annotations"

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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.

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Compte tenu de l'impact sévère et généralisé de la dépression, des initiatives de recherche significatives ont été entreprises pour définir des systèmes d'évaluation automatisée de la dépression. La recherche présentée dans cette thèse tourne autour des questions suivantes qui restent relativement inexplorées malgré leur pertinence dans le domaine de l'évaluation automatisée de la dépression : (1) le rôle de la structure du discours dans l'analyse de la santé mentale, (2) la pertinence de la représentation de l'entrée pour les capacités prédictives des modèles de réseaux neuronaux, et (3) l'importance de l'expertise du domaine dans la détection automatisée de la dépression.La nature dyadique des entretiens patient-thérapeute garantit la présence d'une structure sous-jacente complexe dans le discours. Dans cette thèse, nous établissons d'abord l'importance des questions du thérapeute dans l'entrée du modèle de réseau neuronal, avant de montrer qu'une combinaison séquentielle des entrées du patient et du thérapeute est une stratégie sous-optimale. Par conséquent, des architectures à vues multiples sont proposées comme moyen d'incorporer la structure du discours dans le processus d'apprentissage des réseaux neuronaux. Les résultats expérimentaux obtenus avec deux encodages de texte différents montrent les avantages des architectures multi-vues proposées, validant la pertinence de la conservation de la structure du discours dans le processus d'apprentissage du modèle.Ayant établi la nécessité de conserver la structure du discours dans le processus d'apprentissage, nous explorons plus avant les représentations textuelles basées sur les graphes. Les recherches menées dans ce contexte mettent en évidence l'impact des représentations d'entrée non seulement pour définir les capacités d'apprentissage du modèle, mais aussi pour comprendre leur processus prédictif. Les graphiques de similitude de phrases et les graphiques de corrélation de mots-clés sont utilisés pour illustrer la capacité des représentations graphiques à fournir des perspectives variées sur la même entrée, en mettant en évidence des informations qui peuvent non seulement améliorer les performances prédictives des modèles, mais aussi être pertinentes pour les professionnels de la santé. Le concept de vues multiples est également incorporé dans les deux structures graphiques afin de mettre en évidence les différences de perspectives entre le patient et le thérapeute au cours d'un même entretien. En outre, il est démontré que la visualisation des structures graphiques proposées peut fournir des informations précieuses indiquant des changements subtils dans le comportement du patient et du thérapeute, faisant allusion à l'état mental du patient.Enfin, nous soulignons le manque d'implication des professionnels de la santé dans le contexte de la détection automatique de la dépression basée sur des entretiens cliniques. Dans le cadre de cette thèse, des annotations cliniques de l'ensemble de données DAIC-WOZ ont été réalisées afin de fournir une ressource pour mener des recherches interdisciplinaires dans ce domaine. Des expériences sont définies pour étudier l'intégration des annotations cliniques dans les modèles de réseaux neuronaux appliqués à la tâche de prédiction au niveau des symptômes dans le domaine de la détection automatique de la dépression. En outre, les modèles proposés sont analysés dans le contexte des annotations cliniques afin d'établir une analogie entre leur processus prédictif et leurs tendances psychologiques et ceux des professionnels de la santé, ce qui constitue une étape vers l'établissement de ces modèles en tant qu'outils cliniques fiables
Given 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
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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.

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Velupillai, 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.

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Access to information is fundamental in health care. This thesis presents research on Swedish medical records with the overall goal of building intelligent information access tools that can aid health personnel, researchers and other professions in their daily work, and, ultimately, improve health care in general. The issue of ethics and identifiable information is addressed by creating an annotated gold standard corpus and porting an existing de-identification system to Swedish from English. The aim is to move towards making textual resources available to researchers without risking exposure of patients’ confidential information. Results for the rule-based system are not encouraging, but results for the gold standard are fairly high. Affirmed, uncertain and negated information needs to be distinguished when building accurate information extraction tools. Annotation models are created, with the aim of building automated systems. One model distinguishes certain and uncertain sentences, and is applied on medical records from several clinical departments. In a second model, two polarities and three levels of certainty are applied on diagnostic statements from an emergency department. Overall results are promising. Differences are seen depending on clinical practice, annotation task and level of domain expertise among the annotators. Using annotated resources for automatic classification is studied. Encouraging overall results using local context information are obtained. The fine-grained certainty levels are used for building classifiers for real-world e-health scenarios. This thesis contributes two annotation models of certainty and one of identifiable information, applied on Swedish medical records. A deeper understanding of the language use linked to conveying certainty levels is gained. Three annotated resources that can be used for further research have been created, and implications for automated systems are presented.
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Kholghi, 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.

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This thesis is a step towards automating information extraction from clinical free-text. It establishes a Cost-efficient Enhanced Active Learning framework to significantly reduce annotation cost, while ensuring high-quality extracted information. The practical significance of this research is three-fold: (1) benefitting the overall patient healthcare by facilitating downstream eHealth workflows such as supporting clinical information processing and efficient decision making, (2) benefitting the research in medical informatics by facilitating the development of rich annotated corpora from clinical free text resources, and (3) benefitting the research in machine learning by developing domain-independent and effective active learning approaches.
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Hallier, 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.

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One of the tools a clinician has in disease diagnosis and treatment is genetic testing. To generate value in genetic testing, the link between genetic variants and disease must be discovered, documented, and shared within the community. Working with two existing genomic variation tools, Kafeen and Cordova, a new set of features referred to as Variant-Curation and Database Instantiation (Variant-CADI) was identified, designed, implemented and integrated into the existing Cordova system to unite data collection, management and distribution into one cohesive tool accessible through user interfaces. This eliminates the user needing specialized knowledge of the underlying implementation, data pipeline or data management to collect desired disease specific genetic variations. Using this tool, new disease-specific variation database instances have been initialized and created as demonstrations of the utility of these applications.
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Ghous, Hamid. "Building a robust clinical diagnosis support system for childhood cancer using data mining methods". Thesis, 2016. http://hdl.handle.net/10453/90061.

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University of Technology Sydney. Faculty of Engineering and Information Technology.
Progress 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.
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"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.

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食物中毒感染是常見但非常重要的全球性公共健康問題。沙門氏鼠傷寒桿菌乃常被分離出來的細菌性病原體之一。隨著實驗室參考菌株LT2的基因組序列於2001年被發表之後,另外9個沙門氏鼠傷寒菌菌株的基因序列均已陸續進行測序。最近,本實驗室亦對十個本地沙門氏鼠傷寒菌臨床分離菌株的基因序列進行了測序。為了為這些基因組序列提供高品質的註釋,我們把預測的基因組提交到質量控制工具GenePRIMP以識別有潛在錯誤或異常的預測基因。本研究針對血液分離菌株78896和糞便分離菌株1047518的GenePRIMP報告進行人工檢查,並對每個菌株超過270個的基因進行了修訂。此外,本研究亦對上述的10個本地菌株進行了功能註釋。註釋項目包括沙門氏菌致病島(SPIs)、致病因子、tRNA和非編碼小分子RNA、噬菌體和CRISPRs結構等基因組及致病元素。 KEGG通路則提供了進一步的功能註釋。
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
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Livros sobre o assunto "Clinical annotations"

1

American Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, Ill: AMA Press, 2004.

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American Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, Ill: AMA Press, 2006.

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3

American Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. 2a ed. Chicago, IL: The Association, 2000.

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American Medical Association. Council on Ethical and Judicial Affairs. Code of medical ethics: Current opinions with annotations. Chicago, IL: The Association, 1998.

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5

Oliver 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.

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6

Billroth, 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.

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Affa, AMA Council on Ethical and Judicial, e American Medical Association. Code of Medical Ethics 2004-2005: Current Opinions with Annotations (Code of Medical Ethics). American Medical Association Press, 2004.

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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, 2018.

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9

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. Creative Media Partners, LLC, 2023.

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Billroth, 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.

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Capítulos de livros sobre o assunto "Clinical annotations"

1

Rahman, Fahrurrozi, e Juliana Bowles. "Semantic Annotations in Clinical Guidelines". In 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.

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Del 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". In Lecture Notes in Computer Science, 545–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_48.

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AbstractWhile studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide computationally generated annotations and visual cues to help the pathologist prioritize high-interest image areas. The system includes an image annotation pipeline with DeepHealth-based deep learning models for tissue identification and prostate cancer identification. Annotations are viewed through the platform’s virtual microscope and can be controlled interactively (e.g., thresholding, coloring). Moreover, the platform captures inference provenance information and archives it as RO-Crate artifacts containing data and metadata required for reproducibility. We evaluate the models and the inference pipeline, achieving AUC of 0.986 and 0.969 for tissue and cancer identification, respectively, and verifying linear dependence of execution speed on image tissue content. Finally, we describe the ongoing clinical validation of the contribution, including preliminary results, and discuss feedback from clinical professionals regarding the overall approach.
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Petit, Olivier, Nicolas Thome, Arnaud Charnoz, Alexandre Hostettler e Luc Soler. "Handling Missing Annotations for Semantic Segmentation with Deep ConvNets". In 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.

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Płaczek, Aleksander, Alicja Płuciennik, Mirosław Pach, Michał Jarząb e Dariusz Mrozek. "The Role of Feature Selection in Text Mining in the Process of Discovering Missing Clinical Annotations – Case Study". In 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.

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Cai, Jinzheng, Youbao Tang, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang e Ronald M. Summers. "Accurate Weakly-Supervised Deep Lesion Segmentation Using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST". In 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.

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6

Savova, Guergana, Sameer Pradhan, Martha Palmer, Will Styler, Wendy Chapman e Noémie Elhadad. "Annotating the Clinical Text – MiPACQ, ShARe, SHARPn and THYME Corpora". In Handbook of Linguistic Annotation, 1357–78. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-0881-2_52.

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Kuroda, Makoto, e Keiichi Hiramatsu. "Genome Sequencing and Annotation". In Genomics, Proteomics, and Clinical Bacteriology, 29–45. Totowa, NJ: Humana Press, 2004. http://dx.doi.org/10.1385/1-59259-763-7:029.

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Tinhofer, Ingeborg, Ulrich Keilholz e Damian Rieke. "How to Standardize Molecular Profiling Programs for Routine Patient Care". In 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.

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AbstractManagement of patients with advanced cancer includes individualized treatment recommendations guided by molecular profiles. Refined complex molecular and immunological diagnostics are developed in parallel to the rapidly growing number of targeted therapies for defined genetic alterations and novel immunotherapies. For adequate counseling, patients are presented to Molecular Tumor Boards within the framework of precision oncology programs established at virtually all large cancer centers worldwide. The annotation and clinical interpretation of molecular pathology results are carried out by a multiprofessional team of experts formulating individualized treatment recommendations, taking also into account clinical characteristics. The process of annotation and clinical interpretation of molecular events in tumors also considers predictive factors defined in randomized studies as well as clinical judgement. All steps described above are not standardized, resulting in relevant heterogeneity in treatment recommendations among MTBs in different institutions.In this chapter, contemporary challenges will be discussed, including intratumoral heterogeneity, use of diverse molecular diagnostic systems with inherent differences in sensitivity and specificity of detecting genetic alterations; the yet insufficiently addressed need for harmonizing variant annotation and interpretation; and the currently rather intuitive inclusion of multiple further “soft” parameters; all of which may significantly contribute to the current heterogeneity of recommendations.
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Najafabadipour, Marjan, Juan Manuel Tuñas, Alejandro Rodríguez-González e Ernestina Menasalvas. "Lung Cancer Concept Annotation from Spanish Clinical Narratives". In Lecture Notes in Computer Science, 153–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-06016-9_15.

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Tao, Yimo, Zhigang Peng, Bing Jian, Jianhua Xuan, Arun Krishnan e Xiang Sean Zhou. "Robust Learning-Based Annotation of Medical Radiographs". In 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.

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Trabalhos de conferências sobre o assunto "Clinical annotations"

1

Velupillai, Sumithra. "Semantic annotations in clinical documentation". In the third workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871962.1871968.

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Vizza, Patrizia, Giuseppe Tradigo, Elvis Kallaverja, Maria Giulia Cristofaro, Giuseppe Lucio Cascini e Pierangelo Veltri. "Annotations for clinical data enrichment". In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669480.

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Robinson, Gain, Lin Li, Yimin Zhu, Rachel DuPont, Laura Engstrom, Laxminarayan G. Hegde, Jie Zhang-Hoover, Kimberly Bettano e Antong Chen. "Deep learning-based automated lung tumor segmentation in mouse preclinical micro-CT scans with limited annotations". In Clinical and Biomedical Imaging, editado por Barjor S. Gimi e Andrzej Krol. SPIE, 2024. http://dx.doi.org/10.1117/12.3006521.

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Galveia, Jose N., Antonio Travassos e Luis A. da Silva Cruz. "An Ophthalmology Clinical Decision Support System Based on Clinical Annotations, Ontologies and Images". In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2018. http://dx.doi.org/10.1109/cbms.2018.00024.

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Llorca, Ignacio, Florian Borchert e Matthieu-P. Schapranow. "A Meta-dataset of German Medical Corpora: Harmonization of Annotations and Cross-corpus NER Evaluation". In 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.

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6

Roy, Sharmili, Michael S. Brown e George L. Shih. "Extracting volumetric information from standard two-dimensional radiological annotations within the clinical workflow". In 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2012. http://dx.doi.org/10.1109/bibmw.2012.6470226.

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Schäfer, Henning, Ahmad Idrissi-Yaghir, Peter Horn e 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". In 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.

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Jiang, Zhengqiang, Phuong D. Trieu, Ziba Gandomkar, Seyedamir Tavakoli Taba, Melissa L. Barron e Sarah J. Lewis. "How do you solve a problem like concordance? A study of radiologists’ clinical annotations for mammographic AI training". In Image Perception, Observer Performance, and Technology Assessment, editado por Yan Chen e Claudia R. Mello-Thoms. SPIE, 2023. http://dx.doi.org/10.1117/12.2654147.

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Zhai, Haijun, Todd Lingren, Louise Deleger, Qi Li, Megan Kaiser, Laura Stoutenborough e 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". In 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology (HISB). IEEE, 2012. http://dx.doi.org/10.1109/hisb.2012.31.

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Lee, Min Hun, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino e Sergi Bermúdez i Badia. "Towards Efficient Annotations for a Human-AI Collaborative, Clinical Decision Support System: A Case Study on Physical Stroke Rehabilitation Assessment". In IUI '22: 27th International Conference on Intelligent User Interfaces. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3490099.3511112.

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Relatórios de organizações sobre o assunto "Clinical annotations"

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Xu, Chao, Walter Forkel, Stefan Borgwardt, Franz Baader e 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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Selecting patients for clinical trials is very labor-intensive. Our goal is to develop an automated system that can support doctors in this task. This paper describes a major step towards such a system: the automatic translation of clinical trial eligibility criteria from natural language into formal, logic-based queries. First, we develop a semantic annotation process that can capture many types of clinical trial criteria. Then, we map the annotated criteria to the formal query language. We have built a prototype system based on state-of-the-art NLP tools such as Word2Vec, Stanford NLP tools, and the MetaMap Tagger, and have evaluated the quality of the produced queries on a number of criteria from clinicaltrials.gov. Finally, we discuss some criteria that were hard to translate, and give suggestions for how to formulate eligibility criteria to make them easier to translate automatically.
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