Early Detection of Pancreatic Cancer
Year: 2021
Type: review
Abstract: ABSTRACT: The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.
Source: Pancreas
Authors: Barbara J. Kenner, Natalie D. Abrams, Suresh T. Chari, Bruce F. Field, Ann E. Goldberg, William A. Hoos, David S. Klimstra, Laura J. Rothschild, Sudhir Srivastava, Matthew R. Young, Vay Liang W. Go
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: Foundation for the National Institutes of Health, Massachusetts General Hospital, North Carolina Biotechnology Center, Memorial Sloan-Kettering Cancer Center, Cleveland Clinic, National Institutes of Health, Centers for Disease Control and Prevention, University of Texas MD Anderson Cancer Center, National Cancer Institute
Award: Cancer Center Support Grant
OpenAlex ID: W3207837664