Design and implementation of an intelligent framework for supporting evidence-based treatment recommendations in precision oncology
2020
BACKGROUND: The advances in genome sequencing technologies have provided new opportunities for delivering targeted therapy to patients with advanced cancer. However, these high-throughput assays have also created a multitude of challenges for oncologists in treatment selection, demanding a new approach to support decision-making in clinics.
METHODS: To address this unmet need, this paper describes the design of a symbolic reasoning framework using the method of hierarchical task analysis. Based on this framework, an evidence-based treatment recommendation system was implemented for supporting decision-making based on a patient9s clinicopathologic and biomarker profiles.
RESULTS: This intelligent framework captures a six-step sequential decision process: (1) concept expansion by ontology matching, (2) evidence matching, (3) evidence grading and value-based prioritisation, (4) clinical hypothesis generation, (5) recommendation ranking, and (6) recommendation filtering. The importance of balancing evidence-based and hypothesis-driven treatment recommendations is also highlighted. Of note, tracking history of inference has emerged to be a critical step to allow rational prioritisation of recommendations. The concept of inference tracking also enables the derivation of a novel measure -- level of matching -- that helps to convey whether a treatment recommendation is drawn from incomplete knowledge during the reasoning process.
CONCLUSIONS: This framework systematically encapsulates oncologist9s treatment decision-making process. Further evaluations in prospective clinical studies are warranted to demonstrate how this computational pipeline can be integrated into oncology practice to improve outcomes.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
35
References
0
Citations
NaN
KQI