Joe Kai

144 papers receiving 4.4k citations

Joe Kai's Hit Papers

Can machine-learning improve cardiovascular risk prediction using routine clinical data? 2017 · 820 citations
8200+3+7Years since publication250500750

Peers

Joe Kai
Comparison fields: 5 of 164
  • Health Informatics 149
  • Health Information Management 437
  • Applied Microbiology and Biotechnology 87
  • General Health Professions 978
  • Obstetrics and Gynecology 245
Replace Nirav R. Shah with:
Nirav R. Shah United States
Thomas McGinn United States
Louise Davies United States
Bert Aertgeerts Belgium
Alfonso Iorio Canada
Katie Harron United Kingdom
Thomas Agoritsas Switzerland
Tom Marshall United Kingdom
Sean Tunis United States
Hani Tamim Lebanon
Joe Kai relative to Nirav R. Shah United States Nirav R. Shah's profile →
Citations per field
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Nirav R. Shah · 1×
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 →
2017820
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 →
2015293
3 1996211
4 1996166
5 2007131
6 2013109
7 200694
8 201984
9
Perspectives of people with enduring mental ill health from a community-based qualitative study.
200176
10 201473
11 201968
12 201562
13 201161
14 201361
15 200961
16 199959
17 201358
18 201558
19 201156
20 201453

About Joe Kai

Joe Kai is a scholar working on Public Health, Environmental and Occupational Health, General Health Professions, Surgery, Pediatrics, Perinatology and Child Health and Economics and Econometrics, having authored 147 papers that have together received 4.5k indexed citations. Recurring topics across this work include Lipoproteins and Cardiovascular Health (24 papers), Health Systems, Economic Evaluations, Quality of Life (21 papers), Endometriosis Research and Treatment (11 papers), Cultural Competency in Health Care (11 papers), Childhood Cancer Survivors' Quality of Life (9 papers), Uterine Myomas and Treatments (9 papers), Hemoglobinopathies and Related Disorders (8 papers) and Prenatal Screening and Diagnostics (8 papers). The work is most often cited by research in Health Informatics (149 citations), Health Information Management (437 citations), Applied Microbiology and Biotechnology (87 citations), General Health Professions (978 citations) and Obstetrics and Gynecology (245 citations). Joe Kai has collaborated with scholars based in United Kingdom, Malaysia and Netherlands. Frequent co-authors include Nadeem Qureshi, Stephen Weng, Jenna Reps, Jonathan M. Garibaldi, 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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