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ggbio: an R package for extending the grammar of graphics for genomic data

GENOME BIOLOGY 2012年第8期13卷 R77-R77页

作者： Yin, Tengfei Cook, Dianne Lawrence, Michael Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50011 USA Iowa State Univ Dept Stat Ames IA 50011 USA Genentech Inc Dept Bioinformat San Francisco CA 94080 USA

We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio R package is available at http://***/packages/2.11/bioc/html/***.

关键词： Genome Browser Read Alignment Genomic Viewer common data model Scalable Vector Graphic

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Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2021年第11期28卷 2313-2324页

作者： Na, Jie Zong, Nansu Wang, Chen Midthun, David E. Luo, Yuan Yang, Ping Jiang, Guoqian Mayo Clin Dept Quantitat Hlth Sci Rochester MN 55905 USA Mayo Clin Dept Artificial Intelligence & Informat 200 First St SW Rochester MN 55905 USA Mayo Clin Dept Internal Med Rochester MN 55905 USA Northwestern Univ Dept Prevent Med Chicago IL USA

Objective: The study sought to test the feasibility of conducting a phenome-wide association study to characterize phenotypic abnormalities associated with individuals at high risk for lung cancer using electronic health records. Materials and Methods: We used the beta release of the All of Us Researcher Workbench with clinical and survey data from a population of 225 000 subjects. We identified 3 cohorts of individuals at high risk to develop lung cancer based on (1) the 2013 U.S. Preventive Services Task Force criteria, (2) the long-term quitters of cigarette smoking criteria, and (3) the younger age of onset criteria. We applied the logistic regression analysis to identify the significant associations between individuals' phenotypes and their risk categories. We validated our findings against a lung cancer cohort from the same population and conducted an expert review to understand whether these associations are known or potentially novel. Results: We found a total of 214 statistically significant associations (P< .05 with a Bonferroni correction and odds ratio > 1.5) enriched in the high-risk individuals from 3 cohorts, and 15 enriched in the low-risk individuals. Forty significant associations enriched in the high-risk individuals and 13 enriched in the low-risk individuals were validated in the cancer cohort. Expert review identified 15 potentially new associations enriched in the high-risk individuals. Conclusions: It is feasible to conduct a phenome-wide association study to characterize phenotypic abnormalities associated in high-risk individuals developing lung cancer using electronic health records. The All of Us Research Workbench is a promising resource for the research studies to evaluate and optimize lung cancer screening criteria.

关键词： Phenome Wide Association Study (PheWAS) Electronic Health Records (EHRs) lung cancer screening all of us research program common data model

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The promise of big data for precision population health management in the US

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PUBLIC HEALTH 2020年 185卷 110-116页

作者： Han, A. Isaacson, A. Muennig, P. Columbia Univ Mailman Sch Publ Hlth 722 West 168th StARB 4th Floor New York NY 10032 USA AcademyHealth 1666 K St NW 1100 Washington DC 20006 USA

Objectives: As we enter the year 2020, health data in the United States (US) is still in the process of being curated into a usable format. With coordinated data systems, it becomes possible to answer, with relative certainty, what preventive and medical interventions work in the real world and for whom they might work. Study design: This is a non-systematic expert review. Methods: A non-systematic expert review was undertaken to identify relevant scientific and gray literature on the current state and the limitations of evaluation of health interventions and the health data infrastructure in the US. This review also included the literature on nations with unified data systems. We coupled this review with non-structured interviews of data scientists to gain insight into the progress in establishing the components necessary to support a unified data system and to facilitate data exchange for evaluations, as well as further guide our review. Our goal was to produce a critical analysis of the existing attempts to standardize and use data collected during patient encounters with physicians for public health purposes. Results: data obtained from electronic health records are produced in a way that is challenging to use and difficult to compile across platforms in the US. One response to this problem has been to encourage the exchange and standardization of health record information through Distributed Research Networks and common data models (CDMs). These data can be combined with mobile health, social media, and other sources of data to radically transform what we know about the prevention and management of disease. However, issues with the variety of CDMs and growing sense of distrust of institutions that maintain data continue to impede medical progress. Conclusions: We present a framework for data use that will allow public health to answer a swath of unanswered research questions that can improve public health practice. (C) 2020 The Royal Society for Public Health.

关键词： Population health Quasi-experimental designs data infrastructure data interoperability data standards data linkage common data model Distributed Research Networks mHealth data systems

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RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2024年第SUPPL 2期24卷 259页

作者： Song, Won Hoon Park, Meeyoung Pusan Natl Univ Sch Med Dept Urol Yangsan South Korea Pusan Natl Univ Yangsan Hosp Dept Urol Yangsan South Korea Kyungnam Univ Dept Comp Engn 7 Gyeongnamdaehak Ro Changwon Si 51767 Gyeongsangnam D South Korea

BackgroundThe population diagnosed with renal cell carcinoma, especially in Asia, represents 36.6% of global cases, with the incidence rate of renal cell carcinoma in Korea steadily increasing annually. However, treatment options for renal cell carcinoma are diverse, depending on clinical stage and histologic characteristics. Hence, this study aims to develop a machine learning based clinical decision-support system that recommends personalized treatment tailored to the individual health condition of each *** reviewed the real-world medical data of 1,867 participants diagnosed with renal cell carcinoma between November 2008 and June 2021 at the Pusan National University Yangsan Hospital in South Korea. data were manually divided into a follow-up group where the patients did not undergo surgery or chemotherapy (Surveillance), a group where the patients underwent surgery (Surgery), and a group where the patients received chemotherapy before or after surgery (Chemotherapy). Feature selection was conducted to identify the significant clinical factors influencing renal cell carcinoma treatment decisions from 2,058 features. These features included subsets of 20, 50, 75, 100, and 150, as well as the complete set and an additional 50 expert-selected features. We applied representative machine learning algorithms, namely Decision Tree, Random Forest, and Gradient Boosting Machine (GBM). We analyzed the performance of three applied machine learning algorithms, among which the GBM algorithm achieved an accuracy score of 95% (95% CI, 92-98%) for the 100 and 150 feature sets. The GBM algorithm using 100 and 150 features achieved better performance than the algorithm using features selected by clinical experts (93%, 95% CI 89-97%).ConclusionsWe developed a preliminary personalized treatment decision-support system (TDSS) called "RCC-Supporter" by applying machine learning (ML) algorithms to determine personalized treatment for the various clinical situations of RCC

关键词： Renal cell carcinoma Medical big data Clinical decision support system Machine learning common data model

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A collaborative methodology for developing a semantic model for interlinking Cancer Chemoprevention linked-data sources

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SEMANTIC WEB 2014年第2期5卷 127-142页

作者： Zeginis, Dimitris Hasnain, Ali Loutas, Nikolaos Deus, Helena Futscher Fox, Ronan Tarabanis, Konstantinos Ctr Res & Technol Hellas Thessaloniki Greece Univ Macedonia Informat Syst Lab Thessaloniki Greece Natl Univ Ireland Galway Ireland Digital Enterprise Res Inst Galway Ireland

This paper proposes a collaborative methodology for developing semantic data models. The proposed methodology for the semantic model development follows a "meet-in-the-middle" approach. On the one hand, the concepts emerged in a bottom-up fashion from analyzing the domain and interviewing the domain experts regarding their data needs. On the other hand, it followed a top-down approach whereby existing ontologies, vocabularies and data models were analyzed and integrated with the model. The identified elements were then fed to a multiphase abstraction exercise in order to get the concepts of the model. The derived model is also evaluated and validated by domain experts. The methodology is applied on the creation of the Cancer Chemoprevention semantic model that formally defines the fundamental entities used for annotating and describing inter-connected cancer chemoprevention related data and knowledge resources on the Web. This model is meant to offer a single point of reference for biomedical researchers to search, retrieve and annotate linked cancer chemoprevention related data and web resources. The model covers four areas related to Cancer Chemoprevention: i) concepts from the literature that refer to cancer chemoprevention, ii) facts and resources relevant for cancer prevention, iii) collections of experimental data, procedures and protocols and iv) concepts to facilitate the representation of results related to virtual screening of chemopreventive agents.

关键词： Collaborative model development common data model Cancer Chemoprevention linked data HCLS

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EliIE: An open-source information extraction system for clinical trial eligibility criteria

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2017年第6期24卷 1062-1071页

作者： Kang, Tian Zhang, Shaodian Tang, Youlan Hruby, Gregory W. Rusanov, Alexander Elhadad, Noemie Weng, Chunhua Columbia Univ Dept Biomed Informat 622 W 168 StPH 20Room 407 New York NY 10032 USA Columbia Univ Inst Human Nutr 622 W 168 StPH 20Room 407 New York NY 10032 USA

To develop an open-source information extraction system called Eligibility Criteria Information Extraction (EliIE) for parsing and formalizing free-text clinical research eligibility criteria (EC) following Observational Medical Outcomes Partnership common data model (OMOP CDM) version 5.0. EliIE parses EC in 4 steps: (1) clinical entity and attribute recognition, (2) negation detection, (3) relation extraction, and (4) concept normalization and output structuring. Informaticians and domain experts were recruited to design an annotation guideline and generate a training corpus of annotated EC for 230 Alzheimer's clinical trials, which were represented as queries against the OMOP CDM and included 8008 entities, 3550 attributes, and 3529 relations. A sequence labeling-based method was developed for automatic entity and attribute recognition. Negation detection was supported by NegEx and a set of predefined rules. Relation extraction was achieved by a support vector machine classifier. We further performed terminology-based concept normalization and output structuring. In task-specific evaluations, the best F1 score for entity recognition was 0.79, and for relation extraction was 0.89. The accuracy of negation detection was 0.94. The overall accuracy for query formalization was 0.71 in an end-to-end evaluation. This study presents EliIE, an OMOP CDM-based information extraction system for automatic structuring and formalization of free-text EC. According to our evaluation, machine learning-based EliIE outperforms existing systems and shows promise to improve.

关键词： natural language processing machine learning clinical trials patient selection common data model named entity recognition

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Criteria2Query: a natural language interface to clinical databases for cohort definition

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2019年第4期26卷 294-305页

作者： Yuan, Chi Ryan, Patrick B. Ta, Casey Guo, Yixuan Li, Ziran Hardin, Jill Makadia, Rupa Jin, Peng Shang, Ning Kang, Tian Weng, Chunhua Columbia Univ Dept Biomed Informat 622 West 168th StPH-20Room 407 New York NY 10032 USA Nanjing Univ Sci & Technol Dept Comp Sci & Technol Nanjing Jiangsu Peoples R China Janssen Res & Dev Epidemiol Analyt Titusville NJ USA

Objective Cohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases. Materials and Methods Criteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, transforms it first into a structured criteria representation and next into sharable and executable clinical data queries represented as SQL queries conforming to the OMOP common data model. Users can interactively review, refine, and execute queries in the ATLAS web application. To test effectiveness, we evaluated 125 criteria across different disease domains from *** and 52 user-entered criteria. We evaluated F1 score and accuracy against 2 domain experts and calculated the average computation time for fully automated query formulation. We conducted an anonymous survey evaluating usability. Results Criteria2Query achieved 0.795 and 0.805 F1 score for entity recognition and relation extraction, respectively. Accuracies for negation detection, logic detection, entity normalization, and attribute normalization were 0.984, 0.864, 0.514 and 0.793, respectively. Fully automatic query formulation took 1.22 seconds/criterion. More than 80% (11+ of 13) of users would use Criteria2Query in their future cohort definition tasks. Conclusions We contribute a novel natural language interface to clinical databases. It is open source and supports fully automated and interactive modes for autonomous data-driven cohort definition by researchers with minimal human effort. We demonstrate its promising user friendliness and usability.

关键词： cohort definition natural language processing natural language interfaces to database common data model

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Haematology Outcomes Network in Europe (HONEUR)-A collaborative, interdisciplinary platform to harness the potential of real-world data in hematology

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EUROPEAN JOURNAL OF HAEMATOLOGY 2022年第2期109卷 138-145页

作者： Bardenheuer, Kristina Van Speybroeck, Michel Hague, Clare Nikai, Enkeleida Price, Martin Janssen Pharmaceut NV Beerse Belgium

Introduction There remains a need to optimize treatments and improve outcomes among patients with hematologic malignancies. The timely synthesis and analysis of real-world data could play a key role. Objectives The Haematology Outcomes Network in Europe (HONEUR) is a federated data network (FDN) that aims to overcome the challenges of heterogenous data collected from different registries, hospitals, and other databases in different countries. It has the functionality required to analyze data from various sources in a time efficient manner, while preserving local data security and governance. With this, research studies can be performed that can increase knowledge and understanding of the management of patients with hematologic malignancies. Methods HONEUR uses the Observational Medical Outcomes Partnership (OMOP) common data model, which allows analysis scripts to be run by multiple sites using their own data, ultimately generating aggregated results. Furthermore, distributed analytics can be used to run statistical analyses across multiple sites, as if data were pooled. The external governance model ensures high-quality standards, while data ownership is retained locally. Twenty partners from nine countries are now participating, with data from more than 26 000 patients available for analysis. Research questions that can be addressed through HONEUR include assessments of natural disease history, treatment patterns, and clinical effectiveness. Conclusions The HONEUR FDN marks an important step forward in increasing the value of information routinely captured by individual hospitals, registries and other database holders, thus enabling larger-scale studies to be undertaken rapidly and efficiently.

关键词： common data model data analysis evidence generation federated data network Haematology Outcomes Network in Europe Observational Medical Outcomes Partnership real-world evidence

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allofus: an R package to facilitate use of the All of Us Researcher Workbench

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第12期31卷 3013-3021页

作者： Smith, Louisa H. Cavanaugh, Robert Northeastern Univ Roux Inst 100 Fore St Portland ME 04101 USA Northeastern Univ Bouve Coll Hlth Sci Dept Publ Hlth & Hlth Sci Boston MA 02115 USA

Objectives Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time *** audience All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership common data model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the *** We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.

关键词： All of Us R package common data model OMOP CDM OHDSI

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FOUNTAIN: a modular research platform for integrated real-world evidence generation

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 224页

作者： Oberprieler, Nikolaus G. Pladevall-Vila, Manel Johannes, Catherine Layton, J. Bradley Golozar, Asieh Lavallee, Martin Liu, Fangfang Kubin, Maria Vizcaya, David Bayer AS Oslo Norway RTI Hlth Solut Barcelona Spain Henry Ford Hlth Syst Ctr Hlth Policy & Hlth Serv Res Detroit MI USA RTI Hlth Solut Waltham MA USA RTI Hlth Solut Res Triangle Pk NC USA Odysseus Inc New York NY USA Bayer AG Wuppertal Germany Bayer Pharmaceut St Joan Despi Spain

BackgroundReal-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through *** paper's aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN;FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform's strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)-based program. FOUNTAIN's overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its ***, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase

关键词： Cohort study common data model FOUNTAIN Harmonization Methodology Real-world data

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