Cargando…
Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research
We developed a system to automate analysis of the clinical oncology scientific literature from bibliographic databases and match articles to specific patient cohorts to answer specific questions regarding the efficacy of a treatment. The approach attempts to replicate a clinician’s mental processes...
Autores principales: | Saiz, Fernando Suarez, Sanders, Corey, Stevens, Rick, Nielsen, Robert, Britt, Michael, Yuravlivker, Leemor, Preininger, Anita M., Jackson, Gretchen P. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society of Clinical Oncology
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8140792/ https://www.ncbi.nlm.nih.gov/pubmed/33439724 http://dx.doi.org/10.1200/CCI.20.00087 |
Ejemplares similares
-
Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients
por: Alexander, Marliese, et al.
Publicado: (2020) -
Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study
por: Haddad, Tufia, et al.
Publicado: (2021) -
Comparison of an oncology clinical decision-support system’s recommendations with actual treatment decisions
por: Suwanvecho, Suthida, et al.
Publicado: (2021) -
NEW-ONSET ATRIAL FIBRILLATION DURING HOSPITALIZATION FOR COVID-19 PATIENTS WITHOUT PREEXISTING CARDIOVASCULAR DISEASE
por: Wang, Suwei, et al.
Publicado: (2021) -
Searching for Clinically Relevant Biomarkers in Geriatric Oncology
por: Katsila, Theodora, et al.
Publicado: (2018)