Shital Shah

3.1k total citations
34 papers, 499 citations indexed

About

Shital Shah is a scholar working on Epidemiology, Emergency Medicine and Anesthesiology and Pain Medicine. According to data from OpenAlex, Shital Shah has authored 34 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Epidemiology, 9 papers in Emergency Medicine and 5 papers in Anesthesiology and Pain Medicine. Recurrent topics in Shital Shah's work include Data-Driven Disease Surveillance (8 papers), Influenza Virus Research Studies (5 papers) and Cardiac Arrest and Resuscitation (5 papers). Shital Shah is often cited by papers focused on Data-Driven Disease Surveillance (8 papers), Influenza Virus Research Studies (5 papers) and Cardiac Arrest and Resuscitation (5 papers). Shital Shah collaborates with scholars based in United States, India and United Kingdom. Shital Shah's co-authors include Andrew Kusiak, Gary D. Peksa, Michael Gottlieb, Dallas Holladay, Dhananjay G. Thombare, Michael Waddell, Lucas Joppa, Bistra Dilkina, Ashish Kapoor and Damali Nakitende and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Osteoporosis International and Annals of Emergency Medicine.

In The Last Decade

Shital Shah

30 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shital Shah United States 13 106 102 67 57 53 34 499
Yuichiro Toda Japan 15 58 0.5× 81 0.8× 45 0.7× 184 3.2× 112 2.1× 100 743
Ioannis Koutroulis United States 15 100 0.9× 38 0.4× 61 0.9× 42 0.7× 80 1.5× 45 672
R. Ramanathan India 12 15 0.1× 63 0.6× 105 1.6× 168 2.9× 74 1.4× 69 845
Dingwen Li United States 8 15 0.1× 56 0.5× 34 0.5× 43 0.8× 33 0.6× 16 384
Jorge D. Rios Mexico 16 48 0.5× 47 0.5× 102 1.5× 10 0.2× 87 1.6× 56 918
Yuanfang Ren United States 12 52 0.5× 144 1.4× 30 0.4× 39 0.7× 57 1.1× 40 554
Qing Pan China 15 98 0.9× 116 1.1× 170 2.5× 34 0.6× 92 1.7× 64 794
Jörg Walter Germany 11 19 0.2× 74 0.7× 46 0.7× 78 1.4× 19 0.4× 36 435
Luis Eduardo Mújica Delgado Spain 18 16 0.2× 119 1.2× 36 0.5× 114 2.0× 12 0.2× 68 1.3k
Dong Woo Seo South Korea 18 42 0.4× 10 0.1× 156 2.3× 157 2.8× 202 3.8× 63 1.1k

Countries citing papers authored by Shital Shah

Since Specialization
Citations

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

Fields of papers citing papers by Shital Shah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shital Shah

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

All Works

20 of 20 papers shown
1.
Shah, Shital, et al.. (2024). Addressing Data Poisoning and Model Manipulation Risks using LLM Models in Web Security. 1–6. 1 indexed citations
2.
Shah, Shital, et al.. (2021). Mass-Casualty Training Exercise Using High-Fidelity Computerized Simulators and Involving Time and Resource Limitation. Prehospital and Disaster Medicine. 36(3). 313–320. 1 indexed citations
3.
Turchetta, Matteo, Andrey Kolobov, Shital Shah, Andreas Krause, & Alekh Agarwal. (2020). Safe Reinforcement Learning via Curriculum Induction. Neural Information Processing Systems. 33. 12151–12162. 2 indexed citations
4.
Gottlieb, Michael, et al.. (2019). Impact of endotracheal tube twisting on the diagnostic accuracy of ultrasound for intubation confirmation. The American Journal of Emergency Medicine. 38(7). 1332–1334. 18 indexed citations
5.
Gottlieb, Michael, et al.. (2019). Accuracy of ultrasound for endotracheal intubation between different transducer types. The American Journal of Emergency Medicine. 37(12). 2182–2185. 16 indexed citations
6.
Gottlieb, Michael, et al.. (2018). Variation in the accuracy of ultrasound for the detection of intubation by endotracheal tube size. The American Journal of Emergency Medicine. 37(4). 706–709. 15 indexed citations
7.
Peksa, Gary D., et al.. (2017). A Comparison of Insulin Doses for the Treatment of Hyperkalemia in Patients with Renal Insufficiency. Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy. 37(12). 1516–1522. 36 indexed citations
8.
Gottlieb, Michael, et al.. (2017). Comparison of color flow with standard ultrasound for the detection of endotracheal intubation. The American Journal of Emergency Medicine. 36(7). 1166–1169. 17 indexed citations
9.
Glover, Crystal M., et al.. (2015). Medicaid beneficiaries who continue to use the ED: a focus on the Illinois Medical Home Network. The American Journal of Emergency Medicine. 34(2). 197–201. 3 indexed citations
10.
Shah, Shital, et al.. (2015). Clinical Predictors for Laboratory-Confirmed Influenza Infections: Exploring Case Definitions for Influenza-Like Illness. Infection Control and Hospital Epidemiology. 36(3). 241–248. 27 indexed citations
11.
Shah, Shital, et al.. (2013). Comparing the accuracy of syndrome surveillance systems in detecting influenza-like illness: GUARDIAN vs. RODS vs. electronic medical record reports. Artificial Intelligence in Medicine. 59(3). 169–174. 10 indexed citations
12.
Silva, Julio, et al.. (2011). Disease model fitness and threshold creation for surveillance of infectious diseases. 4(0). 1 indexed citations
13.
14.
Shah, Shital, et al.. (2010). A Practical Approach towards Muffler Design, Development and Prototype Validation. SAE technical papers on CD-ROM/SAE technical paper series. 1. 31 indexed citations
15.
Shah, Shital & Andrew Kusiak. (2010). Relabeling algorithm for retrieval of noisy instances and improving prediction quality. Computers in Biology and Medicine. 40(3). 288–299. 3 indexed citations
16.
Shah, Shital & Andrew Kusiak. (2006). Cancer gene search with data-mining and genetic algorithms. Computers in Biology and Medicine. 37(2). 251–261. 104 indexed citations
17.
Kusiak, Andrew & Shital Shah. (2006). Data-mining-based system for prediction of water chemistry faults. IEEE Transactions on Industrial Electronics. 53(2). 593–603. 27 indexed citations
18.
Kusiak, Andrew, et al.. (2005). DETECTION OF EVENTS CAUSING PLUGGAGE OF A COAL-FIRED BOILER: A DATA MINING APPROACH. Combustion Science and Technology. 177(12). 2327–2348. 4 indexed citations
19.
Shah, Shital, Andrew Kusiak, & Michael A. O’Donnell. (2005). Patient-recognition data-mining model for BCG-plus interferon immunotherapy bladder cancer treatment. Computers in Biology and Medicine. 36(6). 634–655. 14 indexed citations
20.
Shah, Shital & Andrew Kusiak. (2004). Data mining and genetic algorithm based gene/SNP selection. Artificial Intelligence in Medicine. 31(3). 183–196. 71 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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