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M. Mynier
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P2.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 207)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Advanced Diseases - NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P2.01-041 - MD Anderson Oncology Expert Advisor™ System (OEA™): A Cognitive Computing Recommendations Application (App) for Lung Cancer (ID 3106)
09:30 - 09:30 | Author(s): M. Mynier
- Abstract
Background:
The OEA[TM] is a clinical support system with a continuous improvement capability. Its objectives are to enable/empower evidence-based decisions/care by disseminating knowledge and expertise to physicians/users tailored to meet the clinical needs of individual patients as if consulting with an expert. Cognitive computing platforms have the potential to disseminate expert knowledge and tertiary level care to patients. This objective is made possible by making available to physicians/providers cognitive computing generated expert recommendations in diagnosis, staging and treatment. The cognitive computing software was trained by MD Anderson experts using currently available consensus guidelines and an iterative feedback process. Here we test the capability of this cognitive computing software program developed at MD Anderson to generate expert recommendations when patients with advanced-stage NSCLC have a targetable molecular aberration.
Methods:
We developed a web based prototype of MD Anderson’s Oncology Expert Advisor (OEA[TM]), a cognitive clinical decision support tool powered by IBM Watson. The Watson technology is IBM’s third generation cognitive computing system based on its unique capabilities in natural language processing and deep QA (question-answer). We trained OEA[TM] by loading historical patient cases and assessed the accuracy of targeted treatment suggestions using MD Anderson’s physicians’ decisions as benchmark. A false positive result was defined as a treatment recommendation rendered with high confidence that was non-correct (less optimal), whereas false negative was defined as a correct or more optimal treatment suggestion listed as a low confidence recommendation.
Results:
In our preliminary analyses, OEA[TM] demonstrated four core capabilities: 1) Patient Evaluation through interpretation of structured and unstructured clinical data to create a dynamic case summary with longitudinal view of the pertinent events 2) Treatment and management suggestions based on patient profile weighed against consensus guidelines, relevant literature, and MD Anderson expertise, which included approved therapies, genomic based therapies as well as automated matching to appropriate clinical trials at MD Anderson, 3) Care pathway advisory that alerts the user for anticipated toxicities and its early identification and proactive management, and 4) Patient-oriented research functionalities for identification of patient cohorts and hypothesis generation for future potential clinical investigations. Detailed testing continues and the accuracy of standard-of-care (SOC) treatment recommendations of OEA[TM], as well as false positivity and negativity rates will be presented in detail at the meeting.
Conclusion:
OEA[TM] is able to generate dynamic patient case summary by interpreting structured and unstructured clinical data and suggest personalized treatment options. Live system evaluation of OEA[TM] is ongoing and the application of OEA[TM] in clinical practice is expected to be piloted at our institution.
