Joe Kai

143 papers receiving 4.5k citations

Joe Kai's Hit Papers

Can machine-learning improve cardiovascular risk prediction using routine clinical data? 2017 · 848 citations
8480+3+7Years since publication250500750

Peers

Joe Kai
Comparison fields: 5 of 161
  • Health Informatics 121
  • Health Information Management 344
  • Applied Microbiology and Biotechnology 87
  • Critical Care and Intensive Care Medicine 125
  • Obstetrics and Gynecology 187
Replace Thomas McGinn with:
Thomas McGinn United States
Leora I. Horwitz United States
Ethan A. Halm United States
Louise Davies United States
Alex Bottle United Kingdom
Trudy van der Weijden Netherlands
Sean Tunis United States
Michael J. Schull Canada
Anupam B. Jena United States
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Joe Kai relative to Thomas McGinn United States Thomas McGinn's profile →
Citations per field
00.5×3.3×
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Citations per year

Countries citing papers authored by Joe Kai

Since Specialization
Citations

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

Fields of papers citing papers by Joe Kai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Joe Kai, 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 Joe Kai Line = papers co-authored together Joe Kai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 147 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Hit paper breakdown →
2017848
2
A randomised controlled trial of the clinical effectiveness and cost-effectiveness of the levonorgestrel-releasing intrauterine system in primary care against standard treatment for menorrhagia: the ECLIPSE trial
Hit paper breakdown →
2015299
3 1996211
4 1996166
5 2007134
6 2013109
7 200695
8 201989
9
Perspectives of people with enduring mental ill health from a community-based qualitative study.
200178
10 201473
11 201968
12 201364
13 201564
14 201161
15 200961
16 199960
17 201559
18 201359
19 201158
20 201453

About Joe Kai

Joe Kai is a scholar working on Surgery, Public Health, Environmental and Occupational Health, Pediatrics, Perinatology and Child Health, Genetics and General Health Professions, having authored 147 papers that have together received 4.6k indexed citations. Recurring topics across this work include Lipoproteins and Cardiovascular Health (22 papers), Uterine Myomas and Treatments (9 papers), Hemoglobinopathies and Related Disorders (8 papers), Cultural Competency in Health Care (8 papers), BRCA gene mutations in cancer (6 papers), Genomics and Rare Diseases (6 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (5 papers) and Childhood Cancer Survivors' Quality of Life (5 papers). The work is most often cited by research in Health Informatics (121 citations), Health Information Management (344 citations), Applied Microbiology and Biotechnology (87 citations), Critical Care and Intensive Care Medicine (125 citations) and Obstetrics and Gynecology (187 citations). Joe Kai has collaborated with scholars based in United Kingdom, Malaysia and United States. Frequent co-authors include Nadeem Qureshi, Stephen Weng, Jonathan M. Garibaldi, Jenna Reps, Ralph Kwame Akyea, Jane Daniels, Helen Pattison, Ann Crosland, Janesh Gupta and Lee Middleton. Their work appears in journals such as British Journal of General Practice, BMJ Open, Medical Education, PLoS ONE and Cochrane Database of Systematic Reviews.

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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