{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:37:10Z","timestamp":1740202630969,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Treatment recommendation is a nontrivial task &amp;ndash; 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.<\/jats:p>","DOI":"10.3233\/978-1-61499-564-7-300","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T18:09:19Z","timestamp":1740161359000},"source":"Crossref","is-referenced-by-count":0,"title":["A Decision Fusion Framework for Treatment Recommendation Systems"],"prefix":"10.3233","author":[{"family":"Mei Jing","sequence":"additional","affiliation":[]},{"family":"Liu Haifeng","sequence":"additional","affiliation":[]},{"family":"Li Xiang","sequence":"additional","affiliation":[]},{"family":"Xie Guotong","sequence":"additional","affiliation":[]},{"family":"Yu Yiqin","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2015: eHealth-enabled Health"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T18:22:43Z","timestamp":1740162163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-563-0&spage=300&doi=10.3233\/978-1-61499-564-7-300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-564-7-300","relation":{},"ISSN":["0926-9630"],"issn-type":[{"value":"0926-9630","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}