A Decision Fusion Framework for Treatment Recommendation Systems.
Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.
IBM Research, Beijing, China.
Jing
J
Mei
IBM Research, Beijing, China.
Haifeng
H
Liu
IBM Research, Beijing, China.
Xiang
X
Li
IBM Research, Beijing, China.
Guotong
G
Xie
IBM Research, Beijing, China.
Yiqin
Y
Yu
D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003661:Decision Support Techniques; D057286:Electronic Health Records / Q000458:organization & administration*; D005103:Expert Systems; D058436:Knowledge Management; D000069550:Machine Learning; D017146:Models, Organizational; D009323:Natural Language Processing; D013813:Therapy, Computer-Assisted / Q000379:methods*