دورية أكاديمية
أعرض في EDS

An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.

التفاصيل البيبلوغرافية
العنوان: An Intelligent Search & Retrieval System (IRIS) and Clinical and Research Repository for Decision Support Based on Machine Learning and Joint Kernel-based Supervised Hashing.
المؤلفون: Foran DJ; Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA., Chen W; Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA., Kurc T; Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA., Gupta R; Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA., Kaczmarzyk JR; Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA., Torre-Healy LA; Stony Brook University Renaissance School of Medicine, Stony Brook, NY, USA., Bremer E; Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA., Ajjarapu S; VA Healthcare System Jamaica Plain Campus, Boston, MA, USA., Do N; VA Healthcare System Jamaica Plain Campus, Boston, MA, USA., Harris G; New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA., Stroup A; New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA., Durbin E; Kentucky Cancer Registry, Markey Cancer Center, Lexington, KY, USA., Saltz JH; Department of Biomedical Informatics, Stony Brook University, The State University of New York, Stony Brook, NY, USA.
المصدر: Cancer informatics [Cancer Inform] 2024 Feb 04; Vol. 23, pp. 11769351231223806. Date of Electronic Publication: 2024 Feb 04 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: SAGE Publications Country of Publication: United States NLM ID: 101258149 Publication Model: eCollection Cited Medium: Print ISSN: 1176-9351 (Print) Linking ISSN: 11769351 NLM ISO Abbreviation: Cancer Inform Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: <2016- > : Thousand Oaks, CA : SAGE Publications
Original Publication: [Auckland, N.Z.] : Libertas Academica, [2005]-
مستخلص: Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities in oncology. To facilitate the next generation of advances in cancer biology, precision oncology and the population sciences it will be necessary to develop and implement data management and analytic tools that empower investigators to reliably and objectively detect, characterize and chronicle the phenotypic and genomic changes that occur during the transformation from the benign to cancerous state and throughout the course of disease progression. To facilitate these efforts it is incumbent upon the informatics community to establish the workflows and architectures that automate the aggregation and organization of a growing range and number of clinical data types and modalities ranging from new molecular and laboratory tests to sophisticated diagnostic imaging studies. In an attempt to meet those challenges, leading health care centers across the country are making steep investments to establish enterprise-wide, data warehouses. A significant limitation of many data warehouses, however, is that they are designed to support only alphanumeric information. In contrast to those traditional designs, the system that we have developed supports automated collection and mining of multimodal data including genomics, digital pathology and radiology images. In this paper, our team describes the design, development and implementation of a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide actionable insight into the underlying characteristics of the tumor environment that would not be revealed using standard methods and tools. The System features a flexible Extract, Transform and Load (ETL) interface that enables it to adapt to aggregate data originating from different clinical and research sources depending on the specific EHR and other data sources utilized at a given deployment site.
Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
(© The Author(s) 2024.)
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معلومات مُعتمدة: UH3 CA225021 United States CA NCI NIH HHS; P30 CA072720 United States CA NCI NIH HHS; U24 CA180924 United States CA NCI NIH HHS; P30 CA177558 United States CA NCI NIH HHS; U24 CA215109 United States CA NCI NIH HHS; UG3 CA225021 United States CA NCI NIH HHS; UL1 TR003017 United States TR NCATS NIH HHS
فهرسة مساهمة: Keywords: Multi-modal clinical research data warehouse; adaptable extraction; content based retrieval; decision support; large-scale multi-site collaboration; machine learning; transform and load interface
تواريخ الأحداث: Date Created: 20240207 Latest Revision: 20240426
رمز التحديث: 20240426
مُعرف محوري في PubMed: PMC10840403
DOI: 10.1177/11769351231223806
PMID: 38322427
قاعدة البيانات: MEDLINE
الوصف
تدمد:1176-9351
DOI:10.1177/11769351231223806