Yijun Shao

704 citations
52 papers · 429 indexed · h-index 11
Topics
Machine Learning in Healthcare (16 papers)Dementia and Cognitive Impairment Research (6 papers)Chronic Disease Management Strategies (5 papers)

In The Last Decade

Yijun Shao

43 papers receiving 421 citations

Peers

Yijun Shao
Comparison fields: 5 of 118
  • Artificial Intelligence 122
  • Psychiatry and Mental health 57
  • Molecular Biology 46
  • Statistical and Nonlinear Physics 41
  • Media Technology 40
Replace Annelaura Bach Nielsen with:
Annelaura Bach Nielsen Denmark
Ian H. Jarman United Kingdom
Samah Fodeh United States
Ashin Mukherjee United States
Hsien-Wei Ting Taiwan
Chengyin Ye China
Seok‐Jae Heo South Korea
Gina Barnes United States
Shinjini Kundu United States
Stephen Wu United States
Yijun Shao relative to Annelaura Bach Nielsen Denmark Annelaura Bach Nielsen's profile →
Citations per field
00.5×10×13.3×
Annelaura Bach Nielsen · 1×
Citations per year

Countries citing papers authored by Yijun Shao

Since Specialization
Citations

This map shows the geographic impact of Yijun Shao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yijun Shao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yijun Shao more than expected).

Fields of papers citing papers by Yijun Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yijun Shao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yijun Shao. The network helps show where Yijun Shao may publish in the future.

Co-authorship network of co-authors of Yijun Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Yijun Shao. A scholar is included among the top collaborators of Yijun Shao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yijun Shao. Yijun Shao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 2
2 2
3 5
4 3
5 11
6 0
7 0
8 4
9 5
10 14
11 8
12
Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcome
4
13 60
14 5
15 27
16
Extracting frailty status for post surgical mortality prediction
1
17 5
18
Identification and Use of Frailty Indicators from Text to Examine Associations with Clinical Outcomes Among Patients with Heart Failure.
30
19 4
20 4

About Yijun Shao

Yijun Shao is a scholar working on Health Informatics, Geriatrics and Gerontology and Health Information Management, having authored 52 papers that have together received 429 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (16 papers), Dementia and Cognitive Impairment Research (6 papers) and Chronic Disease Management Strategies (5 papers). The work is most often cited by research in Health Informatics (23 citations), Geriatrics and Gerontology (31 citations) and Health Information Management (31 citations). Yijun Shao has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Qing Zeng‐Treitler, Qing Zeng, Yunbo Zeng, Charlene Weir, Yan Cheng, Bruce E. Bray, Rashmee U. Shah, Andrew Shutes‐David, Debby W. Tsuang and Stephen Thielke. Their work appears in journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Journal of the American College of Cardiology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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