Tal El‐Hay

646 total citations
14 papers, 162 citations indexed

About

Tal El‐Hay is a scholar working on Artificial Intelligence, Signal Processing and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Tal El‐Hay has authored 14 papers receiving a total of 162 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Signal Processing and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Tal El‐Hay's work include Bayesian Modeling and Causal Inference (5 papers), Bayesian Methods and Mixture Models (3 papers) and Time Series Analysis and Forecasting (3 papers). Tal El‐Hay is often cited by papers focused on Bayesian Modeling and Causal Inference (5 papers), Bayesian Methods and Mixture Models (3 papers) and Time Series Analysis and Forecasting (3 papers). Tal El‐Hay collaborates with scholars based in Israel, United States and Australia. Tal El‐Hay's co-authors include Nir Friedman, Raz Kupferman, Chen Yanover, Yaara Goldschmidt, Omer Weissbrod, William R. Bishai, Keira A. Cohen, Ranit Aharonov, Vanisha Munsamy and Thomas Conway and has published in prestigious journals such as Journal of the American Medical Informatics Association, Journal of Machine Learning Research and EBioMedicine.

In The Last Decade

Tal El‐Hay

13 papers receiving 159 citations

Peers

Tal El‐Hay
Ying-Chih Lo Taiwan
Ayush Patel United States
Conor K. Corbin United States
Željko Kraljević United Kingdom
Fengshi Jing China
Frank C. Bennis Netherlands
Monica Isgut United States
Anthony Shek United Kingdom
William Mitchell United States
Chungsoo Kim South Korea
Ying-Chih Lo Taiwan
Tal El‐Hay
Citations per year, relative to Tal El‐Hay Tal El‐Hay (= 1×) peers Ying-Chih Lo

Countries citing papers authored by Tal El‐Hay

Since Specialization
Citations

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

Fields of papers citing papers by Tal El‐Hay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tal El‐Hay. 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 Tal El‐Hay. The network helps show where Tal El‐Hay may publish in the future.

Co-authorship network of co-authors of Tal El‐Hay

This figure shows the co-authorship network connecting the top 25 collaborators of Tal El‐Hay. A scholar is included among the top collaborators of Tal El‐Hay 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 Tal El‐Hay. Tal El‐Hay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
1.
El‐Hay, Tal, Jenna Reps, & Chen Yanover. (2025). Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics. npj Digital Medicine. 8(1). 59–59. 1 indexed citations
2.
Curtin, Catherine, Chen Yanover, Tal El‐Hay, et al.. (2024). Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network. Journal of the American Medical Informatics Association. 31(5). 1051–1061. 3 indexed citations
3.
Wyss, Richard, Chen Yanover, Tal El‐Hay, et al.. (2022). Machine learning for improving high‐dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature. Pharmacoepidemiology and Drug Safety. 31(9). 932–943. 13 indexed citations
4.
Dixon, William G, Anna L. Beukenhorst, Belay Birlie Yimer, et al.. (2019). How the weather affects the pain of citizen scientists using a smartphone app. npj Digital Medicine. 2(1). 105–105. 49 indexed citations
5.
Karavani, Ehud, Tal El‐Hay, Yishai Shimoni, & Chen Yanover. (2019). Comment: Causal Inference Competitions: Where Should We Aim?. Statistical Science. 34(1). 2 indexed citations
6.
Falconer, Erin, Tal El‐Hay, John P. Docherty, et al.. (2017). Integrated multisystem analysis in a mental health and criminal justice ecosystem. PubMed. 2014. 526–33. 3 indexed citations
7.
Falconer, Erin, Tal El‐Hay, John P. Docherty, et al.. (2017). Integrated multisystem analysis in a mental health and criminal justice ecosystem. Health & Justice. 5(1). 4–4. 8 indexed citations
8.
Cohen, Keira A., Tal El‐Hay, Kelly L. Wyres, et al.. (2016). Paradoxical Hypersusceptibility of Drug-resistant M ycobacterium tuberculosis to β-lactam Antibiotics. EBioMedicine. 9. 170–179. 35 indexed citations
9.
El‐Hay, Tal, Omer Weissbrod, Elad Eban, Maurizio Zazzi, & Francesca Incardona. (2014). Structured proportional jump processes. 172–181. 1 indexed citations
10.
Ozery-Flato, Michal, Tal El‐Hay, Ligita Ryliškytė, et al.. (2013). Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome. Diabetology & Metabolic Syndrome. 5(1). 36–36. 11 indexed citations
11.
El‐Hay, Tal, et al.. (2013). Incorporating Expressive Graphical Models in Variational Approximations: Chain-Graphs and Hidden Variables. arXiv (Cornell University). 136–143.
12.
El‐Hay, Tal, et al.. (2012). Mean Field Variational Approximation for Continuous-Time Bayesian Networks. Journal of Machine Learning Research. 11(93). 2745–100. 17 indexed citations
13.
El‐Hay, Tal, Nir Friedman, Daphne Koller, & Raz Kupferman. (2012). Continuous Time Markov Networks. arXiv (Cornell University). 3 indexed citations
14.
El‐Hay, Tal, et al.. (2010). Continuous-time belief propagation. International Conference on Machine Learning. 41(2). 343–350. 16 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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2026