Early Detection of Pancreatic Cancer: Applying Artificial Intelligence to Electronic Health Records. Issue 7 (11th August 2021)

• Record Type: Journal Article
• Authors: Kenner, Barbara J.; Abrams, Natalie D.; Chari, Suresh T.; Field, Bruce F.; Goldberg, Ann E.; Hoos, William A.; Klimstra, David S.; Rothschild, Laura J.; Srivastava, Sudhir; Young, Matthew R.; Go, Vay Liang W.
• Is Part Of: Pancreas. Volume 50:Issue 7(2021)
• Page Start: 916
• Page End: 922
• Publication Date: 2021-08-11
• Subjects: artificial intelligence -- electronic health records -- machine learning -- natural language processing -- pancreatic cancer -- early detection; Pancreas -- Diseases -- Periodicals; Pancreas -- Periodicals; Neuroendocrine tumors -- Periodicals; 616.37005
• DOI: 10.1097/MPA.0000000000001882
• Languages: English
• ISSNs: 0885-3177

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.