Andrea Batch

635 citations
18 papers · 477 · h-index 13

Impact in

Papers in

Andrea Batch

17 papers receiving 464 citations

Peers

Andrea Batch
Comparison fields: 5 of 61
  • Human-Computer Interaction 147
  • Computer Vision and Pattern Recognition 305
  • Computer Graphics and Computer-Aided Design 11
  • Health Information Management 13
  • Economics and Econometrics 24
Replace H. Lam with:
H. Lam Canada
Xinhuan Shu China
Emily Wall United States
Fereshteh Amini Canada
Heidi Lam Canada
Paolo Federico Austria
Hirotada Ueda Japan
Stephanie Santosa Canada
Christa Sommerer Austria
John E. Wenskovitch United States
Andrea Batch relative to H. Lam Canada H. Lam's profile →
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Citations per year

Countries citing papers authored by Andrea Batch

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Batch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Andrea Batch, 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 Andrea Batch Line = papers co-authored together Andrea Batch links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 201994
2 202277
3 201768
4 201854
5 202332
6 202024
7 202323
8 202316
9 201815
10
Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators
201915
11 202314
12
Clinical Concept Value Sets and Interoperability in Health Data Analytics.
201813
13 202113
14 202311
15 20244
16 20183
17 20191
18 20230

About Andrea Batch

Andrea Batch is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Computer Graphics and Computer-Aided Design, Economics and Econometrics and Signal Processing, having authored 18 papers that have together received 477 indexed citations. Recurring topics across this work include Data Visualization and Analytics (13 papers), Virtual Reality Applications and Impacts (2 papers), Electronic Health Records Systems (1 paper), Healthcare Policy and Management (1 paper), Forecasting Techniques and Applications (1 paper), Biomedical Text Mining and Ontologies (1 paper), Time Series Analysis and Forecasting (1 paper) and Human Pose and Action Recognition (1 paper). The work is most often cited by research in Human-Computer Interaction (147 citations), Computer Vision and Pattern Recognition (305 citations), Computer Graphics and Computer-Aided Design (11 citations), Health Information Management (13 citations) and Economics and Econometrics (24 citations). Andrea Batch has collaborated with scholars based in United States, United Kingdom and Denmark. Frequent co-authors include Niklas Elmqvist, Tim Dwyer, Kim Marriott, Maxime Cordeil, Bruce Hunter Thomas, Andrew Cunningham, Panagiotis D. Ritsos, Harald Reiterer, Johannes Zagermann and Sebastian Hubenschmid. Their work appears in journals such as IEEE Transactions on Visualization and Computer Graphics, Health Affairs, Computer Graphics Forum, IEEE Computer Graphics and Applications and NBER Chapters.

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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