Yoni Halpern

1.8k citations
16 papers · 838 · h-index 9

Impact in

Papers in

Yoni Halpern

16 papers receiving 819 citations

Peers

Yoni Halpern
Comparison fields: 5 of 128
  • Health Informatics 62
  • Health Information Management 123
  • Family Practice 38
  • Artificial Intelligence 489
  • Issues, ethics and legal aspects 12
Replace Yacine Jernite with:
Yacine Jernite United States
Jason Fries United States
Hercules Dalianis Sweden
Abhishek Pandey India
Stephen Wu United States
Szymon Wilk Poland
Matthew B. A. McDermott United States
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Citations per field
00.5×3.2×
Yacine Jernite · 1×
Citations per year

Countries citing papers authored by Yoni Halpern

Since Specialization
Citations

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

Fields of papers citing papers by Yoni Halpern

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yoni Halpern. 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 Yoni Halpern. The network helps show where Yoni Halpern may publish in the future.

Co-authors

The 25 scholars most cited alongside Yoni Halpern, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yoni Halpern Line = papers co-authored together Yoni Halpern links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2017235
2 2017202
3 201696
4 202085
5 201174
6
Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.
201744
7
Using Anchors to Estimate Clinical State without Labeled Data.
201432
8 201925
9 201916
10 20188
11
Unsupervised learning of noisy-or Bayesian networks
20137
12 20164
13
Text Embeddings Contain Bias. Here's Why That Matters.
20183
14 20173
15 20193
16
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models.
20191

About Yoni Halpern

Yoni Halpern is a scholar working on Artificial Intelligence, Health Information Management, Molecular Biology, Issues, ethics and legal aspects and Public Health, Environmental and Occupational Health, having authored 16 papers that have together received 838 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (4 papers), Topic Modeling (4 papers), Machine Learning in Healthcare (4 papers), Natural Language Processing Techniques (3 papers), Electronic Health Records Systems (3 papers), Nursing Diagnosis and Documentation (3 papers), Artificial Intelligence in Healthcare (2 papers) and Statistical Methods and Bayesian Inference (1 paper). The work is most often cited by research in Health Informatics (62 citations), Health Information Management (123 citations), Family Practice (38 citations), Artificial Intelligence (489 citations) and Issues, ethics and legal aspects (12 citations). Yoni Halpern has collaborated with scholars based in United States and Canada. Frequent co-authors include David Sontag, Steven Horng, Abdulhakim Tlimat, Larry Nathanson, Yacine Jernite, Nathan I. Shapiro, Timothy D. Barfoot, D. Sculley, Alexander D’Amour and Hansa Srinivasan. Their work appears in journals such as Journal of the American Medical Informatics Association, International Journal of Medical Informatics, Scientific Reports, The International Journal of Robotics Research and PLoS ONE.

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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