Ewa Sieliwonczyk: h-index, Total Citations, and Citation Map
Ewa Sieliwonczyk's h-index is 10 (10 i10-index, 311+ total citations across 5+ publications) according to Google Scholar as of May 2026. Ewa Sieliwonczyk is affiliated with Universiteit Antwerpen.
Ewa Sieliwonczyk is a researcher affiliated with Universiteit Antwerpen, specializing in medical genetics, cardiology. Their work has been cited 311 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ewa Sieliwonczyk's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 10
- i10-Index
- 10
- Total Citations
- 311
- Citing Countries
- 18
As of May 2026.
Ewa Sieliwonczyk has an h-index of 10 and 311 total citations across 5 publications, with research cited by institutions in 18 countries.
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Top Cited Works
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Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study
202475
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed AI-enabled ECG models for cardiovascular risk estimation, expanding the framework to identify sex-related risk continua and predict incident hypertension.
The researcher advanced the clinical application of frailty indexes and screening instruments within Belgian primary care settings, establishing a foundational framework for geriatric assessment in community-based practice.
The researcher developed an AI-enhanced ECG method to derive body mass index as a predictor of future cardiometabolic disease, establishing a novel non-invasive screening approach.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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