@inProceedings{
 title = {Web Based Health Recommender System Using Rough Sets, Survival Analysis and Rule-Based Expert Systems},
 type = {inProceedings},
 year = {2007},
 identifiers = {[object Object]},
 keywords = {recommender system,rough sets,survival analysis},
 pages = {491-499},
 websites = {http://link.springer.com/chapter/10.1007/978-3-540-72530-5_59},
 id = {8c3714b6-0c5d-3cf5-bf99-f122d09652e0},
 created = {2017-11-27T14:07:17.240Z},
 file_attached = {true},
 profile_id = {3504b5a6-de0e-3a52-80f0-66c751f3dfa4},
 group_id = {ac658b7b-801a-3b57-b84b-9f63886d241b},
 last_modified = {2017-11-29T18:03:59.200Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Pattaraintakorn2007},
 private_publication = {false},
 abstract = {We propose a health recommendation system architecture using rough sets, survival analysis approaches and rule-based expert systems. Our main goal is to recommend clinical examinations for patients or physicians from patients’ self reported data. Such data will be treated as condition attributes, while survival time from a follow-up study will be treated as the target function. We have amalgamated rough set theory, relational databases, statistics, soft computing and several pertinent techniques to generate a hybrid intelligent system for survival analysis. This study represents the completion of our system by adding a recommendation module.},
 bibtype = {inProceedings},
 author = {Pattaraintakorn, Puntip and Zaverucha, Gregory M. and Cercone, Nick},
 booktitle = {Rough Sets, Fuzzy Sets, Data Mining and Granular Computing}
}

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