Prashant Warier

1.4k total citations · 1 hit paper
5 papers, 845 citations indexed

About

Prashant Warier is a scholar working on Surgery, Artificial Intelligence and Neurology. According to data from OpenAlex, Prashant Warier has authored 5 papers receiving a total of 845 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Surgery, 2 papers in Artificial Intelligence and 1 paper in Neurology. Recurrent topics in Prashant Warier's work include Machine Learning in Healthcare (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper) and Organ Transplantation Techniques and Outcomes (1 paper). Prashant Warier is often cited by papers focused on Machine Learning in Healthcare (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper) and Organ Transplantation Techniques and Outcomes (1 paper). Prashant Warier collaborates with scholars based in United States, Sweden and India. Prashant Warier's co-authors include Pooja Rao, Rohit Ghosh, Sasank Chilamkurthy, Vidur Mahajan, Swetha Tanamala, Norbert G. Campeau, Vasantha Kumar Venugopal, Benjamin J. Vaccaro, Nihar R. Desai and Tariq Ahmad and has published in prestigious journals such as The Lancet, Journal of the American Heart Association and Journal of Cardiac Failure.

In The Last Decade

Prashant Warier

5 papers receiving 827 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study

2018 626 citations Sasank Chilamkurthy, Rohit Ghosh et al. The Lancet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Prashant Warier United States	4	290	216	185	165	160	5	845
Rohit Ghosh United States	6	284 1.0×	216 1.0×	183 1.0×	165 1.0×	158 1.0×	8	886
Vasantha Kumar Venugopal India	7	325 1.1×	227 1.1×	168 0.9×	170 1.0×	164 1.0×	23	785
Vidur Mahajan India	7	334 1.2×	191 0.9×	160 0.9×	194 1.2×	151 0.9×	18	799
Sehyo Yune South Korea	11	343 1.2×	181 0.8×	108 0.6×	94 0.6×	162 1.0×	22	778
Sasank Chilamkurthy United States	4	268 0.9×	184 0.9×	161 0.9×	176 1.1×	132 0.8×	6	663
Swetha Tanamala United States	5	283 1.0×	185 0.9×	159 0.9×	169 1.0×	142 0.9×	8	662
Shahein Tajmir United States	13	505 1.7×	222 1.0×	101 0.5×	89 0.5×	271 1.7×	19	1.2k
Hendrikus J. A. van Os Netherlands	12	120 0.4×	127 0.6×	307 1.7×	62 0.4×	132 0.8×	31	806
Michelle Livne Germany	10	205 0.7×	161 0.7×	166 0.9×	77 0.5×	102 0.6×	17	702
Mohammad Mansouri United States	10	218 0.8×	127 0.6×	89 0.5×	94 0.6×	85 0.5×	20	514

Countries citing papers authored by Prashant Warier

Since Specialization

Citations

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

Fields of papers citing papers by Prashant Warier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prashant Warier

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

All Works

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5 of 5 papers shown

1.

Miller, P. Elliott, Sumeet Pawar, Benjamin J. Vaccaro, et al.. (2019). Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database. Journal of Cardiac Failure. 25(6). 479–483. 51 indexed citations

2.

Chilamkurthy, Sasank, Rohit Ghosh, Swetha Tanamala, et al.. (2018). Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. The Lancet. 392(10162). 2388–2396. 626 indexed citations breakdown →

3.

Reddy, Bhargava, et al.. (2018). Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays. arXiv (Cornell University). 7 indexed citations

4.

Vaccaro, Benjamin J., et al.. (2018). Variation in practice patterns and outcomes across United Network for Organ Sharing allocation regions. Clinical Cardiology. 41(1). 81–86. 2 indexed citations

5.

Ahmad, Tariq, Lars H. Lund, Pooja Rao, et al.. (2018). Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients. Journal of the American Heart Association. 7(8). 159 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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