TY - GEN
T1 - Estimating probability density of content types for promoting medical records search
AU - He, Yun
AU - Hu, Qinmin
AU - Song, Yang
AU - He, Liang
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Disease and symptom in medical records tend to appear in different content types: positive, negative, family history and the others. Traditional information retrieval systems depending on keyword match are often adversely affected by the content types. In this paper, we propose a novel learning approach utilizing the content types as features to improve the medical records search. Particularly, the different contents from the medical records are identified using a Bayesian-based classification method. Then, we introduce our type-based weighting function to take advantage of the content types, in which the weights of the content types are automatically calculated by estimating the probability density functions in the documents. Finally, we evaluate the approach on the TREC 2011 and 2012 Medical Records data sets, in which our experimental results show that our approach is promising and superior.
AB - Disease and symptom in medical records tend to appear in different content types: positive, negative, family history and the others. Traditional information retrieval systems depending on keyword match are often adversely affected by the content types. In this paper, we propose a novel learning approach utilizing the content types as features to improve the medical records search. Particularly, the different contents from the medical records are identified using a Bayesian-based classification method. Then, we introduce our type-based weighting function to take advantage of the content types, in which the weights of the content types are automatically calculated by estimating the probability density functions in the documents. Finally, we evaluate the approach on the TREC 2011 and 2012 Medical Records data sets, in which our experimental results show that our approach is promising and superior.
KW - Content types identification
KW - Density estimation
KW - Medical records search
KW - Weighting function
UR - https://www.scopus.com/pages/publications/84962609489
U2 - 10.1007/978-3-319-30671-1_19
DO - 10.1007/978-3-319-30671-1_19
M3 - 会议稿件
AN - SCOPUS:84962609489
SN - 9783319306704
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 252
EP - 263
BT - Advances in Information Retrieval - 38th European Conference on IR Research, ECIR 2016, Proceedings
A2 - Moens, Marie-Francine
A2 - Ferro, Nicola
A2 - Silvello, Gianmaria
A2 - di Nunzio, Giorgio Maria
A2 - Hauff, Claudia
A2 - Crestani, Fabio
A2 - Mothe, Josiane
A2 - Silvestri, Fabrizio
PB - Springer Verlag
T2 - 38th European Conference on Information Retrieval Research, ECIR 2016
Y2 - 20 March 2016 through 23 March 2016
ER -