Mamta Puppala

623 total citations
26 papers, 365 citations indexed

About

Mamta Puppala is a scholar working on Surgery, Oncology and General Health Professions. According to data from OpenAlex, Mamta Puppala has authored 26 papers receiving a total of 365 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Surgery, 6 papers in Oncology and 5 papers in General Health Professions. Recurrent topics in Mamta Puppala's work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Global Cancer Incidence and Screening (3 papers). Mamta Puppala is often cited by papers focused on AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Global Cancer Incidence and Screening (3 papers). Mamta Puppala collaborates with scholars based in United States, Taiwan and China. Mamta Puppala's co-authors include Stephen T.C. Wong, Tiancheng He, Xiaohui Yu, Jenny C. Chang, Joe Ensor, Tejal Patel, Jorge Darcourt, Angel Rodriguez, Eva Zsigmond and Robert A. Phillips and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Gastroenterology.

In The Last Decade

Mamta Puppala

24 papers receiving 355 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mamta Puppala United States 10 105 92 79 70 62 26 365
Xuan Song China 8 47 0.4× 47 0.5× 54 0.7× 53 0.8× 41 0.7× 30 376
Birju Patel United States 10 33 0.3× 126 1.4× 80 1.0× 96 1.4× 58 0.9× 18 449
David Koff Canada 10 59 0.6× 76 0.8× 41 0.5× 176 2.5× 44 0.7× 28 536
Sarah Enslin United States 6 61 0.6× 136 1.5× 49 0.6× 196 2.8× 22 0.4× 15 593
Jiye An China 11 106 1.0× 80 0.9× 14 0.2× 69 1.0× 70 1.1× 30 423
Sonoo Thadaney-Israni United States 5 32 0.3× 228 2.5× 113 1.4× 128 1.8× 59 1.0× 6 640
Poonam Patil India 7 55 0.5× 114 1.2× 30 0.4× 136 1.9× 19 0.3× 31 356
Ajit Londhe United States 5 39 0.4× 43 0.5× 50 0.6× 17 0.2× 26 0.4× 11 371
Andrew Baer United States 6 101 1.0× 80 0.9× 52 0.7× 18 0.3× 17 0.3× 6 253
Susan Weber United States 12 62 0.6× 131 1.4× 66 0.8× 35 0.5× 28 0.5× 22 634

Countries citing papers authored by Mamta Puppala

Since Specialization
Citations

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

Fields of papers citing papers by Mamta Puppala

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mamta Puppala

This figure shows the co-authorship network connecting the top 25 collaborators of Mamta Puppala. A scholar is included among the top collaborators of Mamta Puppala 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 Mamta Puppala. Mamta Puppala 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.
Aguiar‐Ibáñez, Raquel, et al.. (2023). EPH136 Available Screening Programs for the Early Identification of Cancers: A Targeted Literature Review. Value in Health. 26(6). S188–S189. 1 indexed citations
2.
Jaggupilli, Appalaraju, Bin Yuan, Venkatesh L. Hegde, et al.. (2023). TP-0184 inhibits FLT3/ACVR1 to overcome FLT3 inhibitor resistance and hinder AML growth synergistically with venetoclax. Leukemia. 38(1). 82–95. 1 indexed citations
3.
Puppala, Mamta, et al.. (2022). A comparative efficacy study of diagnostic digital breast tomosynthesis and digital mammography in BI-RADS 4 breast cancer diagnosis. European Journal of Radiology. 153. 110361–110361. 3 indexed citations
4.
Wang, Lin, Guihua Li, Mamta Puppala, et al.. (2022). An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients. Scientific Reports. 12(1). 20633–20633. 8 indexed citations
5.
6.
Darcourt, Jorge, Joe Ensor, Eva Zsigmond, et al.. (2020). Analysis of the Implementation of Telehealth Visits for Care of Patients With Cancer in Houston During the COVID-19 Pandemic. JCO Oncology Practice. 17(1). e36–e43. 80 indexed citations
7.
Masud, Faisal, Tariq Nisar, Jaya Paranilam, et al.. (2019). Sepsis outcomes in solid organ transplant recipients. Transplant Infectious Disease. 22(1). e13214–e13214. 8 indexed citations
8.
He, Tiancheng, Mamta Puppala, Xiaohui Yu, et al.. (2019). A Deep Learning–Based Decision Support Tool for Precision Risk Assessment of Breast Cancer. JCO Clinical Cancer Informatics. 3(3). 1–12. 30 indexed citations
9.
Wang, Lin, Zhong Xue, Mamta Puppala, et al.. (2019). Preventing inpatient falls with injuries using integrative machine learning prediction: a cohort study. npj Digital Medicine. 2(1). 127–127. 23 indexed citations
10.
Frost, Adaani, et al.. (2019). Outcomes of Pulmonary Hypertension Patients Presenting with Sepsis in a Single Large Hospital System. A6505–A6505. 1 indexed citations
11.
Stubbins, Renee, Tiancheng He, Xiaohui Yu, et al.. (2018). A Behavior-Modification, Clinical-Grade Mobile Application to Improve Breast Cancer Survivors' Accountability and Health Outcomes. JCO Clinical Cancer Informatics. 2(2). 1–11. 25 indexed citations
12.
Sinha, Neeraj, J. DeVos, Thomas Kaleekal, et al.. (2017). Early clearance vs persistence of de novo donor‐specific antibodies following lung transplantation. Clinical Transplantation. 31(8). 21 indexed citations
13.
14.
Álvarez, Paulino, Mamta Puppala, Guillermo Torre‐Amione, et al.. (2017). Prevalence of in‐hospital nonsteroidal antiinflammatory drug exposure in patients with a primary diagnosis of heart failure. Cardiovascular Therapeutics. 35(3). 3 indexed citations
15.
Tariq, Nabil, Linda W. Moore, Mamta Puppala, et al.. (2017). Fast track feeding after revisional bariatric surgery is associated with reduced length of stay. Surgery for Obesity and Related Diseases. 13(10). S166–S166. 2 indexed citations
16.
Patel, Tejal, Mamta Puppala, Joe Ensor, et al.. (2016). Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods. Cancer. 123(1). 114–121. 48 indexed citations
17.
He, Tiancheng, et al.. (2016). A smartphone app framework for segmented cancer care coordination. 15. 372–375. 3 indexed citations
18.
Puppala, Mamta, et al.. (2015). Staphylococcus Species Leads the Eskape Infections in Various Hematological Malignancies. Blood. 126(23). 4623–4623.
19.
Shirkey, Beverly A., Linda W. Moore, Mamta Puppala, et al.. (2015). Early Feeding Post Bariatric Surgery Reduces Length of Stay. Surgery for Obesity and Related Diseases. 11(6). S14–S15. 1 indexed citations
20.
Puppala, Mamta, et al.. (2015). METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine. IEEE Transactions on Biomedical Engineering. 62(12). 2776–2786. 36 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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