Saurabh Johri

898 total citations · 1 hit paper
5 papers, 468 citations indexed

About

Saurabh Johri is a scholar working on Artificial Intelligence, Family Practice and Health Information Management. According to data from OpenAlex, Saurabh Johri has authored 5 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Family Practice and 1 paper in Health Information Management. Recurrent topics in Saurabh Johri's work include Machine Learning in Healthcare (2 papers), Clinical Reasoning and Diagnostic Skills (2 papers) and Geochemistry and Geologic Mapping (1 paper). Saurabh Johri is often cited by papers focused on Machine Learning in Healthcare (2 papers), Clinical Reasoning and Diagnostic Skills (2 papers) and Geochemistry and Geologic Mapping (1 paper). Saurabh Johri collaborates with scholars based in United Kingdom and United States. Saurabh Johri's co-authors include Jonathan G. Richens, Ciarán M. Lee, Yura Perov, Joanne R. Winter, Christopher D. Hart, Christopher Hart, Robert Walecki, Felix Bragman and Yuanzhao Zhang and has published in prestigious journals such as Nature Communications, IEEE Transactions on Neural Networks and Learning Systems and Frontiers in Artificial Intelligence.

In The Last Decade

Saurabh Johri

5 papers receiving 453 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.

Improving the accuracy of medical diagnosis with causal machine learning

2020 355 citations Jonathan G. Richens, Ciarán M. Lee et al. Nature Communications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saurabh Johri United Kingdom	5	191	90	78	54	38	5	468
Peter Schulam United States	10	219 1.1×	115 1.3×	49 0.6×	75 1.4×	24 0.6×	23	496
Artuur Leeuwenberg Netherlands	8	275 1.4×	123 1.4×	77 1.0×	58 1.1×	21 0.6×	23	579
Afnan Almuhanna Saudi Arabia	5	113 0.6×	212 2.4×	118 1.5×	45 0.8×	40 1.1×	27	434
Jonathan G. Richens United Kingdom	7	236 1.2×	42 0.5×	56 0.7×	42 0.8×	35 0.9×	10	482
Dana Moukheiber United States	8	138 0.7×	174 1.9×	94 1.2×	34 0.6×	44 1.2×	15	432
Jiebin Chen China	10	84 0.4×	108 1.2×	69 0.9×	41 0.8×	46 1.2×	19	417
Yuanfang Ren United States	12	144 0.8×	77 0.9×	37 0.5×	37 0.7×	29 0.8×	40	554
Yuanda Zhu United States	11	208 1.1×	104 1.2×	143 1.8×	29 0.5×	27 0.7×	38	461
Yilin Ning Singapore	17	230 1.2×	150 1.7×	104 1.3×	89 1.6×	25 0.7×	58	822
Umberto Maniscalco Italy	8	96 0.5×	165 1.8×	81 1.0×	42 0.8×	67 1.8×	32	482

Countries citing papers authored by Saurabh Johri

Since Specialization

Citations

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

Fields of papers citing papers by Saurabh Johri

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saurabh Johri

This figure shows the co-authorship network connecting the top 25 collaborators of Saurabh Johri. A scholar is included among the top collaborators of Saurabh Johri 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 Saurabh Johri. Saurabh Johri 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.

Hart, Christopher, et al.. (2022). Leveraging Directed Causal Discovery to Detect Latent Common Causes in Cause-Effect Pairs. IEEE Transactions on Neural Networks and Learning Systems. 35(4). 4938–4947. 5 indexed citations

2.

Richens, Jonathan G., Ciarán M. Lee, & Saurabh Johri. (2021). Author Correction: Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications. 12(1). 2108–2108. 6 indexed citations

3.

Richens, Jonathan G., Ciarán M. Lee, & Saurabh Johri. (2020). Improving the accuracy of medical diagnosis with causal machine learning. Nature Communications. 11(1). 3923–3923. 355 indexed citations breakdown →

4.

Perov, Yura, et al.. (2020). A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis. Frontiers in Artificial Intelligence. 3. 543405–543405. 96 indexed citations

5.

Zhang, Yuanzhao, Robert Walecki, Joanne R. Winter, et al.. (2020). Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: The Utility of Language Models. Frontiers in Digital Health. 2. 569261–569261. 6 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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