نتائج البحث - "Diagnostic decision support"

العلاقة: Lipnitskaya, N. Fundamentals for the Intelligent Non-Invasive Diagnostics = Основания интеллектуальной неинвазивной диагностики / N. Lipnitskaya, V. Rostovtsev // Открытые семантические технологии проектирования интеллектуальных систем = Open Semantic Technologies for Intelligent Systems (OSTIS) : сборник научных трудов / Белорусский государственный университет информатики и радиоэлектроники; редкол.: В. В. Голенков [и др.]. – Минск, 2024. – Вып. 8. – С. 149–156.; https://libeldoc.bsuir.by/handle/123456789/55555Test

الإتاحة: https://libeldoc.bsuir.by/handle/123456789/55555Test

View record in BASE

5

دورية أكاديمية

Clinician-centric diagnosis of rare genetic diseases: performance of a gene pertinence metric in decision support for clinicians

المؤلفون: Michael M. Segal, Renee George, Peter Waltman, Ayman W. El-Hattab, Kiely N. James, Valentina Stanley, Joseph Gleeson

المصدر: Orphanet Journal of Rare Diseases, Vol 15, Iss 1, Pp 1-10 (2020)

مصطلحات موضوعية: Rare disease diagnosis, Diagnostic decision support system, Artificial intelligence, Genomic analysis, Copy number variation, Medicine

وصف الملف: electronic resource

العلاقة: http://link.springer.com/article/10.1186/s13023-020-01461-1Test; https://doaj.org/toc/1750-1172Test

الوصول الحر: https://doaj.org/article/602ee97181f24459813280ff8f4b5023Test

View record in DOAJ

النص الكامل

6

دورية أكاديمية

Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

المؤلفون: de Causans, Alix, Carré, Alexandre, Roux, Alexandre, Tauziède-Espariat, Arnault, Ammari, Samy, Dezamis, Edouard, Dhermain, Frederic, Reuzé, Sylvain, Deutsch, Eric, Oppenheim, Catherine, Varlet, Pascale, Pallud, Johan, Edjlali, Myriam, Robert, Charlotte

المساهمون: Radiothérapie Moléculaire et Innovation Thérapeutique (RaMo-IT), Institut Gustave Roussy (IGR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay

المصدر: ISSN: 2234-943X ; Frontiers in Oncology ; https://hal.science/hal-04514880Test ; Frontiers in Oncology, 2021, 11, ⟨10.3389/fonc.2021.638262⟩.

مصطلحات موضوعية: radiomics machine learning glioblastoma brain metastasis diagnostic decision support system, radiomics, machine learning, glioblastoma, brain metastasis, diagnostic decision support system, [PHYS]Physics [physics], [INFO]Computer Science [cs], [SDV]Life Sciences [q-bio]

العلاقة: hal-04514880; https://hal.science/hal-04514880Test; https://hal.science/hal-04514880/documentTest; https://hal.science/hal-04514880/file/fonc-11-638262.pdfTest

الإتاحة: https://doi.org/10.3389/fonc.2021.638262Test
https://hal.science/hal-04514880Test
https://hal.science/hal-04514880/documentTest
https://hal.science/hal-04514880/file/fonc-11-638262.pdfTest

View record in BASE

7

دورية أكاديمية

Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

المؤلفون: Alix de Causans, Alexandre Carré, Alexandre Roux, Arnault Tauziède-Espariat, Samy Ammari, Edouard Dezamis, Frederic Dhermain, Sylvain Reuzé, Eric Deutsch, Catherine Oppenheim, Pascale Varlet, Johan Pallud, Myriam Edjlali, Charlotte Robert

المصدر: Frontiers in Oncology, Vol 11 (2021)

مصطلحات موضوعية: radiomics, machine learning, glioblastoma, brain metastasis, diagnostic decision support system, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fonc.2021.638262/fullTest; https://doaj.org/toc/2234-943XTest

الوصول الحر: https://doaj.org/article/baa77d49e80d436e888563b9842deef0Test

View record in DOAJ

النص الكامل

8

تقرير

Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics ; Artificial Intelligence in Health Care ; Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics ; Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics: Report to Congressional Requesters ; GAO Technology Assessment ; Technology Assessment ; Report to Congressional Requesters

المؤلفون: United States. Government Accountability Office., National Academy of Medicine (U.S.)

المساهمون: Howard, Karen L., McGinnis, J. Michael

مصطلحات موضوعية: Artificial intelligence -- Medical applications -- United States, Machine learning, Medical informatics -- United States, AI, ML, AI/ML, artificial intelligence, health care, National Academy of Medicine, AI-assisted diagnostic decision support tools, AI-DDS

جغرافية الموضوع: United States

الوقت: 2020-11/2022-09

وصف الملف: xi, 88 pages : color illustrations; Text

العلاقة: rep-no: GAO-22-104629; local-cont-no: 0546-D; local-cont-no: 001250786; sudoc: GA 1.13:GAO-22-104629; oclc: 1409572379; url: https://www.gao.gov/assets/gao-22-104629.pdfTest; https://digital.library.unt.edu/ark:/67531/metadc2288717Test/; ark: ark:/67531/metadc2288717

الإتاحة: https://digital.library.unt.edu/ark:/67531/metadc2288717Test/

View record in BASE