ML Bots
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Cardiovascular Health and Disease Prevention
- Blood Pressure and Hypertension Studies
- Heart Failure Treatment and Management
Papers in
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- Cardiovascular Health and Disease Prevention 3
- Cardiovascular Disease and Adiposity 2
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- Migration, Health and Trauma 2
- Co-authors
- Hester M. den Ruijter (1 shared paper)Gerard Pasterkamp (1 shared paper)Sanne A. E. Peters (1 shared paper)KGM Moons (1 shared paper)Steven W J Nijman (1 shared paper)Artuur M. Leeuwenberg (1 shared paper)Folkert Wouter Asselbergs (1 shared paper)Thomas P. A. Debray (1 shared paper)
- Journals
- European Journal of Preventive Cardiology (2 papers)European Journal of Public Health (1 paper)Journal of Clinical Epidemiology (1 paper)BMC Cardiovascular Disorders (1 paper)BMC Public Health (1 paper)
- Partner nations
- NetherlandsGermanyUnited Kingdom
In The Last Decade
ML Bots
8 papers receiving 380 citations
Peers
Comparison fields: 5 of 100
- Health Informatics 16
- Cardiology and Cardiovascular Medicine 100
- Health Information Management 14
- Family Practice 3
- Nutrition and Dietetics 24
Countries citing papers authored by ML Bots
This map shows the geographic impact of ML Bots'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 ML Bots with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites ML Bots more than expected).
Fields of papers citing papers by ML Bots
This network shows the impact of papers produced by ML Bots. 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 ML Bots. The network helps show where ML Bots may publish in the future.
Co-authors
The 25 scholars most cited alongside ML Bots, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 161 | |
| 2 | 2015 | 113 | |
| 3 | 2002 | 45 | |
| 4 | 2010 | 36 | |
| 5 | 2009 | 24 | |
| 6 | 2013 | 7 | |
| 7 | 2013 | 1 | |
| 8 | Ultrasound measurement of atherosclerosis - Response : Carotid intima-media thickness measurements in intervention studies | 2004 | 1 |
| 9 | 2007 | 0 |
About ML Bots
ML Bots is a scholar working on Cardiology and Cardiovascular Medicine, Clinical Psychology, General Health Professions, Health and Pulmonary and Respiratory Medicine, having authored 9 papers that have together received 388 indexed citations. Recurring topics across this work include Cardiovascular Health and Disease Prevention (3 papers), Cardiovascular Disease and Adiposity (2 papers), Employment and Welfare Studies (2 papers), Migration, Health and Trauma (2 papers), Cerebrovascular and Carotid Artery Diseases (1 paper), Healthcare Systems and Practices (1 paper), Peripheral Artery Disease Management (1 paper) and Health disparities and outcomes (1 paper). The work is most often cited by research in Health Informatics (16 citations), Cardiology and Cardiovascular Medicine (100 citations), Health Information Management (14 citations), Family Practice (3 citations) and Nutrition and Dietetics (24 citations). ML Bots has collaborated with scholars based in Netherlands, Germany and United Kingdom. Frequent co-authors include Hester M. den Ruijter, Gerard Pasterkamp, Sanne A. E. Peters, KGM Moons, Steven W J Nijman, Artuur M. Leeuwenberg, Folkert Wouter Asselbergs, Thomas P. A. Debray, Inés Beekers and Ilonca C. H. Vaartjes. Their work appears in journals such as European Journal of Preventive Cardiology, European Journal of Public Health, Journal of Clinical Epidemiology, BMC Cardiovascular Disorders and BMC Public Health.
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.