Derick M. Oliveira: h-index, Total Citations, and Citation Map
Derick M. Oliveira's h-index is 12 (13 i10-index, 2,019+ total citations across 5+ publications) according to Google Scholar as of May 2026. Derick M. Oliveira is affiliated with Universidade Federal de Minas Gerais.
Derick M. Oliveira is a researcher affiliated with Universidade Federal de Minas Gerais, specializing in ciência da computação. Their work has been cited 2,019 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Derick M. Oliveira's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 12
- i10-Index
- 13
- Total Citations
- 2,019
- Citing Countries
- 10
As of May 2026.
Derick M. Oliveira has an h-index of 12 and 2,019 total citations across 5 publications, with research cited by institutions in 10 countries.
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Global Impact Map
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Top Cited Works
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Automatic diagnosis of the 12-lead ECG using a deep neural network
20201,331
Top Citing Countries
Top Citing Institutions
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 a deep neural network for automatic 12-lead ECG diagnosis, establishing a foundational approach for AI-driven cardiac analysis as evidenced by high independent citation rates.
The researcher pioneered the integration of tele-electrocardiography with big data analytics, establishing a foundational framework for assessing clinical outcomes in digital ECG monitoring.
The researcher developed a deep neural network method to estimate electrocardiographic age as a predictor of mortality, establishing a novel biomarker for cardiovascular risk assessment.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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