William La Cava

2.1k total citations
61 papers, 793 citations indexed

About

William La Cava is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine and Mechanical Engineering. According to data from OpenAlex, William La Cava has authored 61 papers receiving a total of 793 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 9 papers in Cardiology and Cardiovascular Medicine and 9 papers in Mechanical Engineering. Recurrent topics in William La Cava's work include Evolutionary Algorithms and Applications (23 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Machine Learning and Data Classification (9 papers). William La Cava is often cited by papers focused on Evolutionary Algorithms and Applications (23 papers), Metaheuristic Optimization Algorithms Research (16 papers) and Machine Learning and Data Classification (9 papers). William La Cava collaborates with scholars based in United States, Portugal and Brazil. William La Cava's co-authors include Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Ryan J. Urbanowicz, Lee Spector, Kourosh Danai, Yi Guo, Jonathan Keller, Thomas Helmuth and Matthew A. Lackner and has published in prestigious journals such as Circulation, Journal of the American College of Cardiology and European Heart Journal.

In The Last Decade

William La Cava

55 papers receiving 763 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
William La Cava United States 14 393 135 95 90 53 61 793
Rahul K. Sevakula India 13 212 0.5× 156 1.2× 300 3.2× 55 0.6× 69 1.3× 35 675
Ling Zhuang China 12 104 0.3× 64 0.5× 81 0.9× 53 0.6× 26 0.5× 45 497
Nikola Anđelić Croatia 16 200 0.5× 203 1.5× 155 1.6× 33 0.4× 20 0.4× 75 899
Yuhui Zhang China 15 260 0.7× 79 0.6× 26 0.3× 32 0.4× 85 1.6× 68 723
Jie Duan China 9 324 0.8× 84 0.6× 73 0.8× 82 0.9× 18 0.3× 25 629
Elena Montañés Spain 11 266 0.7× 78 0.6× 23 0.2× 44 0.5× 23 0.4× 41 592
Ivan Lorencin Croatia 16 197 0.5× 186 1.4× 146 1.5× 32 0.4× 12 0.2× 73 878
D. Dumitrescu Romania 13 260 0.7× 26 0.2× 54 0.6× 40 0.4× 10 0.2× 99 732
Vadim Borisov Russia 9 223 0.6× 85 0.6× 31 0.3× 42 0.5× 13 0.2× 48 652
Torgyn Shaikhina United Kingdom 6 160 0.4× 37 0.3× 23 0.2× 37 0.4× 16 0.3× 7 619

Countries citing papers authored by William La Cava

Since Specialization
Citations

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

Fields of papers citing papers by William La Cava

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William La Cava. 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 William La Cava. The network helps show where William La Cava may publish in the future.

Co-authorship network of co-authors of William La Cava

This figure shows the co-authorship network connecting the top 25 collaborators of William La Cava. A scholar is included among the top collaborators of William La Cava based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with William La Cava. William La Cava is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Teele, Sarah A., Avihu Z. Gazit, Craig Futterman, et al.. (2025). Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients. Critical Care Explorations. 7(1). e1204–e1204. 2 indexed citations
2.
Ghelani, Sunil J., et al.. (2025). Artificial Intelligence-Enabled ECG to Detect Congenitally Corrected Transposition of the Great Arteries. Pediatric Cardiology. 47(3). 1376–1382.
3.
4.
Bomarito, Geoffrey, et al.. (2025). Call for Action: towards the next generation of symbolic regression benchmark. Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2529–2538.
5.
Mayourian, Joshua, William La Cava, Sarah D. de Ferranti, et al.. (2025). Expert-Level Automated Diagnosis of the Pediatric ECG Using a Deep Neural Network. JACC. Clinical electrophysiology. 11(6). 1308–1320. 1 indexed citations
6.
Mayourian, Joshua, William La Cava, Akhil Vaid, et al.. (2024). Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease. Journal of the American College of Cardiology. 84(9). 815–828. 13 indexed citations
7.
Mayourian, Joshua, William La Cava, Tal Geva, et al.. (2024). Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease. European Heart Journal. 46(9). 856–868. 9 indexed citations
8.
França, Fabrício Olivetti de, et al.. (2024). Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. Proceedings of the Genetic and Evolutionary Computation Conference. 896–904. 1 indexed citations
9.
Mayourian, Joshua, William La Cava, Akhil Vaid, et al.. (2024). Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. 149(12). 917–931. 24 indexed citations
10.
Chivers, Corey, et al.. (2024). Intrapartum electronic fetal heart rate monitoring to predict acidemia at birth with the use of deep learning. American Journal of Obstetrics and Gynecology. 232(1). 116.e1–116.e9. 10 indexed citations
11.
Mayourian, Joshua, Robert L. Geggel, William La Cava, Sunil J. Ghelani, & John K. Triedman. (2024). Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects. Pediatric Cardiology. 46(5). 1235–1240. 3 indexed citations
12.
Lett, Elle & William La Cava. (2023). Translating intersectionality to fair machine learning in health sciences. Nature Machine Intelligence. 5(5). 476–479. 12 indexed citations
13.
Cava, William La, et al.. (2023). A flexible symbolic regression method for constructing interpretable clinical prediction models. npj Digital Medicine. 6(1). 107–107. 15 indexed citations
14.
Poteat, Tonia, Elle Lett, Ashleigh J. Rich, et al.. (2023). Effects of Race and Gender Classifications on Atherosclerotic Cardiovascular Disease Risk Estimates for Clinical Decision-Making in a Cohort of Black Transgender Women. Health Equity. 7(1). 803–808. 2 indexed citations
15.
Cava, William La, et al.. (2023). Exploring SLUG: Feature Selection Using Genetic Algorithms and Genetic Programming. SN Computer Science. 5(1). 4 indexed citations
16.
Cava, William La & Jason H. Moore. (2020). Learning feature spaces for regression with genetic programming. Genetic Programming and Evolvable Machines. 21(3). 433–467. 18 indexed citations
17.
Cava, William La, Christopher R. Bauer, Jason H. Moore, & Sarah A. Pendergrass. (2020). Interpretation of machine learning predictions for patient outcomes in electronic health records. PubMed Central. 35 indexed citations
18.
Cava, William La, et al.. (2019). Evaluating recommender systems for AI-driven data science. arXiv (Cornell University). 2 indexed citations
19.
Olson, Randal S., William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, & Jason H. Moore. (2017). PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Mining. 10(1). 36–36. 188 indexed citations
20.
Keller, Jonathan, Yi Guo, William La Cava, H. Link, & B. McNiff. (2012). Gearbox Reliability Collaborative Phase 1 and 2: Testing and Modeling Results; Preprint. University of North Texas Digital Library (University of North Texas). 4 indexed citations

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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