Robert Chew

35 papers receiving 444 citations

Robert Chew's Hit Papers

Data extraction for evidence synthesis using a large language model: A proof‐of‐concept study 2024 · 52 citations
520+1Years since publication1020304050

Peers

Robert Chew
Comparison fields: 5 of 119
  • Health Informatics 37
  • Statistics, Probability and Uncertainty 31
  • Media Technology 22
  • Health 21
  • Transportation 16
Replace Giuseppe Casalicchio with:
Giuseppe Casalicchio Germany
Vibhuti Gupta United States
Lingyao Li United States
Charles T. Gray Australia
Yuqi Si United States
Suvodeep Mazumdar United Kingdom
Michael S. Ringel Switzerland
Shakil Ahmad Saudi Arabia
Prasad Patil United States
Robert Chew relative to Giuseppe Casalicchio Germany Giuseppe Casalicchio's profile →
Citations per field
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Giuseppe Casalicchio · 1×
Citations per year

Countries citing papers authored by Robert Chew

Since Specialization
Citations

This map shows the geographic impact of Robert Chew'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 Robert Chew with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Chew more than expected).

Fields of papers citing papers by Robert Chew

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert Chew. 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 Robert Chew. The network helps show where Robert Chew may publish in the future.

Co-authors

The 25 scholars most cited alongside Robert Chew, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Robert Chew Line = papers co-authored together Robert Chew links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202076
2 201857
3
Data extraction for evidence synthesis using a large language model: A proof‐of‐concept study
Hit paper breakdown →
202452
4 201752
5 201728
6 202423
7 201823
8 201923
9 201920
10 202418
11 202111
12 201911
13 20228
14 20178
15 20188
16 20217
17 20243
18 20193
19 20223
20 20183

About Robert Chew

Robert Chew is a scholar working on Artificial Intelligence, Sociology and Political Science, Infectious Diseases, Communication and Molecular Biology, having authored 37 papers that have together received 455 indexed citations. Recurring topics across this work include Long-Term Effects of COVID-19 (3 papers), Social Media and Politics (3 papers), Meta-analysis and systematic reviews (3 papers), COVID-19 Clinical Research Studies (3 papers), Biomedical Text Mining and Ontologies (3 papers), Mental Health via Writing (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Digital Marketing and Social Media (2 papers). The work is most often cited by research in Health Informatics (37 citations), Statistics, Probability and Uncertainty (31 citations), Media Technology (22 citations), Health (21 citations) and Transportation (16 citations). Robert Chew has collaborated with scholars based in United States, Austria and Netherlands. Frequent co-authors include Annice Kim, Antonio A. Morgan‐López, Meghan Hegarty‐Craver, D. Temple, Maggie O’Neil, Robert Beach, Karen Crotty, Gerald Gartlehner, Rainer Hilscher and Meera Viswanathan. Their work appears in journals such as JMIR Public Health and Surveillance, PLoS Medicine, Research Synthesis Methods, Journal of Medical Internet Research and Sensors.

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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