John Guttag

703 total citations
12 papers, 370 citations indexed

About

John Guttag is a scholar working on Infectious Diseases, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, John Guttag has authored 12 papers receiving a total of 370 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Infectious Diseases, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in John Guttag's work include Clostridium difficile and Clostridium perfringens research (3 papers), Video Analysis and Summarization (2 papers) and Sports Analytics and Performance (2 papers). John Guttag is often cited by papers focused on Clostridium difficile and Clostridium perfringens research (3 papers), Video Analysis and Summarization (2 papers) and Sports Analytics and Performance (2 papers). John Guttag collaborates with scholars based in United States. John Guttag's co-authors include Adrian V. Dalca, Guha Balakrishnan, Mert R. Sabuncu, Jenna Wiens, Erica S. Shenoy, Erin Ryan, Krishna Rao, Robert J. McCaffrey, Maggie Makar and Laraine Washer and has published in prestigious journals such as Circulation, PLoS ONE and Medical Image Analysis.

In The Last Decade

John Guttag

9 papers receiving 363 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Guttag United States	5	168	132	81	71	38	12	370
Karthik V. Sarma United States	9	80 0.5×	186 1.4×	58 0.7×	242 3.4×	51 1.3×	18	446
Atallah Baydoun United States	12	50 0.3×	146 1.1×	47 0.6×	60 0.8×	29 0.8×	27	364
Azira Khalil Malaysia	12	58 0.3×	150 1.1×	56 0.7×	54 0.8×	20 0.5×	24	340
Shinjini Kundu United States	11	50 0.3×	153 1.2×	46 0.6×	173 2.4×	46 1.2×	19	690
Rajagopala Chadaga India	9	68 0.4×	113 0.9×	26 0.3×	139 2.0×	26 0.7×	21	295
Pau Medrano−Gracia New Zealand	16	172 1.0×	447 3.4×	164 2.0×	48 0.7×	62 1.6×	26	867
Maithra Raghu United States	10	102 0.6×	65 0.5×	12 0.1×	188 2.6×	42 1.1×	22	374
Pankaj K. Jain India	11	42 0.3×	222 1.7×	39 0.5×	105 1.5×	70 1.8×	12	454
Di Xiao Australia	16	263 1.6×	691 5.2×	63 0.8×	65 0.9×	30 0.8×	49	947
Dhruv Patel United States	10	63 0.4×	56 0.4×	25 0.3×	71 1.0×	31 0.8×	32	287

Countries citing papers authored by John Guttag

Since Specialization

Citations

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

Fields of papers citing papers by John Guttag

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Guttag

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

All Works

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12 of 12 papers shown

Lu, Haiyan, et al.. (2025). Test-time augmentation improves efficiency in conformal prediction. 20622–20631.

Dalca, Adrian V., et al.. (2022). Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration. PLoS ONE. 17(2). e0263922–e0263922. 4 indexed citations

Kamineni, Meghana, Erkin Ötleş, Krishna Rao, et al.. (2022). Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers. Infection Control and Hospital Epidemiology. 44(7). 1163–1166. 3 indexed citations

Sjoding, Michael W., et al.. (2021). 350. Joint Modeling of EHR and CXR Data to Predict COVID-19 Deterioration. Open Forum Infectious Diseases. 8(Supplement_1). S279–S279.

Guttag, John, et al.. (2021). Multiplying Matrices Without Multiplying. arXiv (Cornell University). 992–1004. 2 indexed citations

Dalca, Adrian V., Guha Balakrishnan, John Guttag, & Mert R. Sabuncu. (2019). Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical Image Analysis. 57. 226–236. 237 indexed citations

Makar, Maggie, Robert J. McCaffrey, Krishna Rao, et al.. (2018). A Generalizable, Data-Driven Approach to Predict Daily Risk ofClostridium difficileInfection at Two Large Academic Health Centers. Infection Control and Hospital Epidemiology. 39(4). 425–433. 89 indexed citations

Guttag, John, et al.. (2013). A data-driven method for in-game decision making in MLB. 973–979. 16 indexed citations

Wiens, Jenna & John Guttag. (2012). Learning Evolving Patient Risk Processes for C. Diff Colonization. International Conference on Machine Learning. 7 indexed citations

10.

Guttag, John, et al.. (2012). Predicting the Next Pitch. 10 indexed citations

11.

Syed, Zeeshan, Benjamin M. Scirica, Satishkumar Mohanavelu, et al.. (2010). Abstract 15908: Association of Heart Rate Turbulence, Deceleration Capacity and Morphologic Variability with Sudden Cardiac Death Following Non-ST-Elevation Acute Coronary Syndrome: Results from the MERLIN-TIMI 36 Trial. Circulation. 122. 88–91.

12.

Syed, Zeeshan, Dorothy Curtis, John Guttag, Francesca Nesta, & Robert A. Levine. (2006). Software Enhanced Learning of Cardiac Auscultation. PubMed. 20. 6105–6108. 2 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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