Ekanath Rangan

872 total citations · 1 hit paper
18 papers, 437 citations indexed

About

Ekanath Rangan is a scholar working on Cardiology and Cardiovascular Medicine, Biomedical Engineering and Signal Processing. According to data from OpenAlex, Ekanath Rangan has authored 18 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cardiology and Cardiovascular Medicine, 6 papers in Biomedical Engineering and 5 papers in Signal Processing. Recurrent topics in Ekanath Rangan's work include ECG Monitoring and Analysis (6 papers), Non-Invasive Vital Sign Monitoring (6 papers) and Time Series Analysis and Forecasting (5 papers). Ekanath Rangan is often cited by papers focused on ECG Monitoring and Analysis (6 papers), Non-Invasive Vital Sign Monitoring (6 papers) and Time Series Analysis and Forecasting (5 papers). Ekanath Rangan collaborates with scholars based in India, United States and Tunisia. Ekanath Rangan's co-authors include Rahul Krishnan Pathinarupothi, Jonathan H. Chen, Ashwin Nayak, K. P. Soman, E. A. Gopalakrishnan, Maneesha Vinodini Ramesh, M Snyder, K.J.S. Anand, K. A. Unnikrishna Menon and Prakash Ishwar and has published in prestigious journals such as Journal of Medical Internet Research, IEEE Internet of Things Journal and npj Digital Medicine.

In The Last Decade

Ekanath Rangan

17 papers receiving 425 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.

Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine

2024 104 citations Ashwin Nayak, Ekanath Rangan et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ekanath Rangan India	8	131	94	89	82	67	18	437
Jamal El Mhamdi Morocco	11	43 0.3×	133 1.4×	53 0.6×	29 0.4×	18 0.3×	48	491
Rahul Krishnan Pathinarupothi India	11	164 1.3×	49 0.5×	97 1.1×	117 1.4×	6 0.1×	53	500
Muhammed Kürşad Uçar Türkiye	12	116 0.9×	66 0.7×	105 1.2×	19 0.2×	8 0.1×	53	433
Md. Mahbubur Rahman United States	13	113 0.9×	62 0.7×	109 1.2×	24 0.3×	6 0.1×	52	449
Chang‐Won Jeong South Korea	11	82 0.6×	48 0.5×	39 0.4×	26 0.3×	8 0.1×	65	379
Avesh Kumar Singh India	5	129 1.0×	77 0.8×	17 0.2×	22 0.3×	37 0.6×	8	516
Lejla Gurbeta Bosnia and Herzegovina	13	131 1.0×	56 0.6×	19 0.2×	18 0.2×	35 0.5×	23	508
Alramzana Nujum Navaz United Arab Emirates	10	157 1.2×	74 0.8×	7 0.1×	112 1.4×	31 0.5×	24	487
Saisakul Chernbumroong United Kingdom	10	133 1.0×	102 1.1×	21 0.2×	145 1.8×	13 0.2×	21	628
Tamer Ahmed Eltaras Qatar	4	72 0.5×	57 0.6×	10 0.1×	35 0.4×	26 0.4×	6	244

Countries citing papers authored by Ekanath Rangan

Since Specialization

Citations

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

Fields of papers citing papers by Ekanath Rangan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ekanath Rangan

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

All Works

Sort: Min cites: Since: Top N: Style:

18 of 18 papers shown

1.

Stephen, P., et al.. (2025). Fine-Tuning Methods for Large Language Models in Clinical Medicine by Supervised Fine-Tuning and Direct Preference Optimization: Comparative Evaluation. Journal of Medical Internet Research. 27. e76048–e76048.

2.

Nayak, Ashwin, et al.. (2024). Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine. npj Digital Medicine. 7(1). 20–20. 104 indexed citations breakdown →

3.

Rangan, Ekanath, Rahul Krishnan Pathinarupothi, K.J.S. Anand, & M Snyder. (2022). Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning. JAMIA Open. 5(4). ooac080–ooac080. 14 indexed citations

4.

Pathinarupothi, Rahul Krishnan, et al.. (2021). Heart Lung Health Monitor: Remote At-Home Patient Surveillance for Pandemic Management. 127–130. 12 indexed citations

5.

Pathinarupothi, Rahul Krishnan, et al.. (2018). When Less is Better. 4. 116–120. 4 indexed citations

6.

Pathinarupothi, Rahul Krishnan, et al.. (2018). Data to diagnosis in global health: a 3P approach. BMC Medical Informatics and Decision Making. 18(1). 78–78. 7 indexed citations

7.

Pathinarupothi, Rahul Krishnan, et al.. (2018). IoT-Based Smart Edge for Global Health: Remote Monitoring With Severity Detection and Alerts Transmission. IEEE Internet of Things Journal. 6(2). 2449–2462. 142 indexed citations

8.

Pathinarupothi, Rahul Krishnan, et al.. (2018). Deriving High Performance Alerts from Reduced Sensor Data for Timely Intervention in Acute Hypotensive Episodes. PubMed. 101. 3260–3263. 1 indexed citations

9.

Pathinarupothi, Rahul Krishnan, et al.. (2017). Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning. 524–529. 52 indexed citations

10.

Pathinarupothi, Rahul Krishnan, et al.. (2017). Instantaneous heart rate as a robust feature for sleep apnea severity detection using deep learning. 293–296. 56 indexed citations

11.

Pathinarupothi, Rahul Krishnan & Ekanath Rangan. (2017). Consensus motifs as adaptive and efficient predictors for acute hypotensive episodes. PubMed. 33. 1688–1691. 8 indexed citations

12.

Pathinarupothi, Rahul Krishnan, et al.. (2016). RASPRO: rapid summarization for effective prognosis in wireless remote health monitoring. 122–127. 3 indexed citations

13.

Rangan, Ekanath, et al.. (2016). An Experimental Approach towards Energy Sustainability in University Communities. 11. 1 indexed citations

14.

Pathinarupothi, Rahul Krishnan & Ekanath Rangan. (2016). Large scale remote health monitoring in sparsely connected rural regions. 694–700. 7 indexed citations

15.

Rangan, Ekanath, et al.. (2016). Real-time identification & alert of ischemic events in high risk cardiac patients. 12. 1–5. 3 indexed citations

16.

Rangan, Ekanath, et al.. (2016). Real-time and offline techniques for identifying obstructive sleep apnea patients. 101. 1–4. 3 indexed citations

17.

Pathinarupothi, Rahul Krishnan & Ekanath Rangan. (2016). Discovering vital trends for personalized healthcare delivery. 1106–1109. 4 indexed citations

18.

Pathinarupothi, Rahul Krishnan, Maneesha Vinodini Ramesh, & Ekanath Rangan. (2016). Multi-layer architectures for remote health monitoring. 1–6. 16 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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