Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care. (16th November 2018)

• Record Type: Journal Article
• Authors: Simon, George; DiNardo, Courtney D.; Takahashi, Koichi; Cascone, Tina; Powers, Cynthia; Stevens, Rick; Allen, Joshua; Antonoff, Mara B.; Gomez, Daniel; Keane, Pat; Suarez Saiz, Fernando; Nguyen, Quynh; Roarty, Emily; Pierce, Sherry; Zhang, Jianjun; Hardeman Barnhill, Emily; Lakhani, Kate; Shaw, Kenna; Smith, Brett; Swisher, Stephen; High, Rob; Futreal, P. Andrew; Heymach, John; Chin, Lynda
• Is Part Of: Oncologist. Volume 24:Number 6(2019)
• Page Start: 772
• Page End: 782
• Publication Date: 2018-11-16
• Subjects: Artificial intelligence application in medicine -- Virtual expert advisor -- Clinical decision support -- Closing the cancer care gap -- Democratization of evidence‐based care; Oncology -- Periodicals; Tumors -- Periodicals; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Oncology; Tumors; Neoplasms; Electronic journals; Periodicals; Periodicals; 616.994
• DOI: 10.1634/theoncologist.2018-0257
• Languages: English
• ISSNs: 1083-7159

Abstract

Abstract: Background: Rapid advances in science challenge the timely adoption of evidence‐based care in community settings. To bridge the gap between what is possible and what is practiced, we researched approaches to developing an artificial intelligence (AI) application that can provide real‐time patient‐specific decision support. Materials and Methods: The Oncology Expert Advisor (OEA) was designed to simulate peer‐to‐peer consultation with three core functions: patient history summarization, treatment options recommendation, and management advisory. Machine‐learning algorithms were trained to construct a dynamic summary of patients cancer history and to suggest approved therapy or investigative trial options. All patient data used were retrospectively accrued. Ground truth was established for approximately 1, 000 unique patients. The full Medline database of more than 23 million published abstracts was used as the literature corpus. Results: OEA's accuracies of searching disparate sources within electronic medical records to extract complex clinical concepts from unstructured text documents varied, with F1 scores of 90%–96% for non‐time‐dependent concepts (e.g., diagnosis) and F1 scores of 63%–65% for time‐dependent concepts (e.g., therapy history timeline). Based on constructed patient profiles, OEA suggests approved therapy options linked to supporting evidence (99.9% recall; 88% precision), and screens for eligible clinical trials on ClinicalTrials.gov (97.9% recall; 96.9% precision). Conclusion: Our results demonstrated technical feasibility of an AI‐powered application to construct longitudinal patient profiles in context and to suggest evidence‐based treatment and trial options. Our experience highlighted the necessity of collaboration across clinical and AI domains, and the requirement of clinical expertise throughout the process, from design to training to testing. Abstract : Can an application powered by artificial intelligence construct patient profiles in context and suggest evidence‐based treatment options? The Oncology Expert Advisor was designed for such a purpose. Preliminary results of a study using this technological innovation are reported.