TY - JOUR
T1 - Applying Artificial Intelligence to Address the Knowledge Gaps in Cancer Care
AU - Simon, George
AU - DiNardo, Courtney D.
AU - Takahashi, Koichi
AU - Cascone, Tina
AU - Powers, Cynthia
AU - Stevens, Rick
AU - Allen, Joshua
AU - Antonoff, Mara B.
AU - Gomez, Daniel
AU - Keane, Pat
AU - Suarez Saiz, Fernando
AU - Nguyen, Quynh
AU - Roarty, Emily
AU - Pierce, Sherry
AU - Zhang, Jianjun
AU - Hardeman Barnhill, Emily
AU - Lakhani, Kate
AU - Shaw, Kenna
AU - Smith, Brett
AU - Swisher, Stephen
AU - High, Rob
AU - Futreal, P. Andrew
AU - Heymach, John
AU - Chin, Lynda
N1 - Publisher Copyright:
© AlphaMed Press 2018
PY - 2019/6
Y1 - 2019/6
N2 - 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. Implications for Practice: Artificial intelligence (AI)-powered digital advisors such as the Oncology Expert Advisor have the potential to augment the capacity and update the knowledge base of practicing oncologists. By constructing dynamic patient profiles from disparate data sources and organizing and vetting vast literature for relevance to a specific patient, such AI applications could empower oncologists to consider all therapy options based on the latest scientific evidence for their patients, and help them spend less time on information “hunting and gathering” and more time with the patients. However, realization of this will require not only AI technology maturation but also active participation and leadership by clincial experts.
AB - 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. Implications for Practice: Artificial intelligence (AI)-powered digital advisors such as the Oncology Expert Advisor have the potential to augment the capacity and update the knowledge base of practicing oncologists. By constructing dynamic patient profiles from disparate data sources and organizing and vetting vast literature for relevance to a specific patient, such AI applications could empower oncologists to consider all therapy options based on the latest scientific evidence for their patients, and help them spend less time on information “hunting and gathering” and more time with the patients. However, realization of this will require not only AI technology maturation but also active participation and leadership by clincial experts.
KW - Artificial intelligence application in medicine
KW - Clinical decision support
KW - Closing the cancer care gap
KW - Democratization of evidence-based care
KW - Virtual expert advisor
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U2 - 10.1634/theoncologist.2018-0257
DO - 10.1634/theoncologist.2018-0257
M3 - Article
C2 - 30446581
AN - SCOPUS:85056751880
SN - 1083-7159
VL - 24
SP - 772
EP - 782
JO - Oncologist
JF - Oncologist
IS - 6
ER -