TY - GEN
T1 - Potentiality of healthcare big data
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
AU - Wang, Yueyao
AU - Hu, Qinmin
AU - Song, Yang
AU - He, Liang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Clinical Decision Systems utilize patient profiles to search for relevant medical support. Existing work on medical search has a primary topic on query expansion, which enriches queries by adding more useful terms. However, it performs well when queries are concise. In this paper, we aim to tackle verbose queries and propose a new automatic query reformulation method, which not only considers query expansion, but also includes query reduction. This improves the searching performance by refining the query to effectively avoid irrelevant results. The method is achieved by classifying each sentence into expansion and reduction categories with a weighted score model, which depends on the occurrence of medical and negative terms. The refined queries show promising results on experiments with TREC CDS datasets. In particular, the final performance makes improvements in terms of NDCG as 8.2% compared to the prevailing query expansion method, and 22.07% compared to the baseline of original query.
AB - Clinical Decision Systems utilize patient profiles to search for relevant medical support. Existing work on medical search has a primary topic on query expansion, which enriches queries by adding more useful terms. However, it performs well when queries are concise. In this paper, we aim to tackle verbose queries and propose a new automatic query reformulation method, which not only considers query expansion, but also includes query reduction. This improves the searching performance by refining the query to effectively avoid irrelevant results. The method is achieved by classifying each sentence into expansion and reduction categories with a weighted score model, which depends on the occurrence of medical and negative terms. The refined queries show promising results on experiments with TREC CDS datasets. In particular, the final performance makes improvements in terms of NDCG as 8.2% compared to the prevailing query expansion method, and 22.07% compared to the baseline of original query.
KW - Clinical Decision Support
KW - Medical Data Search
KW - Query Reformulation
UR - https://www.scopus.com/pages/publications/85047720933
U2 - 10.1109/BigData.2017.8257996
DO - 10.1109/BigData.2017.8257996
M3 - 会议稿件
AN - SCOPUS:85047720933
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 807
EP - 816
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2017 through 14 December 2017
ER -