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| Recommending Medical Literature with Random Forest Model and Query Expansion |
Ding Hao1,2( ),Hu Guangwei1,2(),Qi Jianglei1,Zhuang Guangguang3 |
1School of Information Management, Nanjing University, Nanjing 210023, China
2Institute of Government Data Resources, Nanjing University, Nanjing 210023, China
3School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China |
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Abstract [Objective] This paper tries to find valuable contents from a large number of medical literatures, aiming to help physicians make diagnosis and improve medical literature recommendation. [Methods] We proposed a new method based on the random forest model and keyword query expansion. First, we used the MeSH dictionary and the automatically constructed acronym dictionary to establish the complete relationship between keywords and corresponding articles at three levels of sentence, paragraph and document. Then, we calculated the multiple similarity between topics and articles. For each article, the PageRank and Authority weights of HITS were calculated through the citation network in the literature set. [Results] Compared with the average of the 10 values with the highest NDCG@100 value from the TREC clinical decision support follow-up evaluation, the overall average difference of the proposed method was within 0.9%, which was very small. [Limitations] Some new literatures or the “Sleeping Beauty” literature may have lower retrieval ranking due to low citation in the early stage. Our method cannot make accurate recommendations for these papers. [Conclusions] The proposed method effectively improves the medical literature recommendation.
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Received: 11 October 2021
Published: 31 December 2021
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| Fund:National Social Science Fund of China(20&ZD154);National Natural Science Foundation of China(71573117);State Grid Jiangsu Electric Power Company Management Consulting Project(SGJSYF00YHJS2000144) |
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Corresponding Authors:
Hu Guangwei,ORCID:0000-0003-1303-363X
E-mail: hugw@nju.edu.cn
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