Qingqing Mao

4.3k total citations
53 papers, 1.1k citations indexed

About

Qingqing Mao is a scholar working on Epidemiology, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Qingqing Mao has authored 53 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Epidemiology, 14 papers in Artificial Intelligence and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Qingqing Mao's work include Sepsis Diagnosis and Treatment (12 papers), Machine Learning in Healthcare (10 papers) and Autism Spectrum Disorder Research (5 papers). Qingqing Mao is often cited by papers focused on Sepsis Diagnosis and Treatment (12 papers), Machine Learning in Healthcare (10 papers) and Autism Spectrum Disorder Research (5 papers). Qingqing Mao collaborates with scholars based in United States, China and United Kingdom. Qingqing Mao's co-authors include Ritankar Das, Jacob Calvert, Jana Hoffman, Melissa Jay, Uli K. Chettipally, Gina Barnes, Anurag Garikipati, Christopher Barton, Yaniv Kerem and Grant Fletcher and has published in prestigious journals such as Applied Physics Letters, The Astrophysical Journal and Scientific Reports.

In The Last Decade

Qingqing Mao

49 papers receiving 1.0k citations

Peers

Qingqing Mao

EPIDE

HIM

SURGE

Yifan Zhou United States

José Roberto Ayala Solares United Kingdom

William K. Thompson United States

Stefano Finazzi Italy

Alexandru Burlacu Romania

Paul D. Clayton United States

Michael Burns United States

Bodil Andersson Sweden

Aaron J. Masino United States

Eric L. Johnson United States

Yifan Zhou United States

Qingqing Mao

211 ×0.6

312 ×0.8

EPIDE

478 ×2.7

36 ×0.3

HIM

67 ×0.6

SURGE

Citations per year, relative to Qingqing Mao Qingqing Mao (= 1×) peers Yifan Zhou

Countries citing papers authored by Qingqing Mao

Since Specialization

Citations

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

Fields of papers citing papers by Qingqing Mao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingqing Mao

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

All Works

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20 of 20 papers shown

Shen, Qiang, et al.. (2025). Objective over Architecture: Fraud Detection Under Extreme Imbalance in Bank Account Opening. Computation. 13(12). 290–290.

Garikipati, Anurag, Yunfan Zhou, Mădălina Ciobanu, et al.. (2024). Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics. 14(11). 1152–1152.

Garikipati, Anurag, Mădălina Ciobanu, Gina Barnes, et al.. (2024). Parent-Led Applied Behavior Analysis to Impact Clinical Outcomes for Individuals on the Autism Spectrum: Retrospective Chart Review. JMIR Pediatrics and Parenting. 7. e62878–e62878.

Ciobanu, Mădălina, Anurag Garikipati, Gina Barnes, et al.. (2024). Family-Centric Applied Behavior Analysis Facilitates Improved Treatment Utilization and Outcomes. Journal of Clinical Medicine. 13(8). 2409–2409. 1 indexed citations

Garikipati, Anurag, Mayank Sharma, Mădălina Ciobanu, et al.. (2024). OpenMedLM: prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models. Scientific Reports. 14(1). 14156–14156. 33 indexed citations

Garikipati, Anurag, Mădălina Ciobanu, Gina Barnes, et al.. (2023). Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease. Diagnostics. 14(1). 13–13. 7 indexed citations

Garikipati, Anurag, et al.. (2023). Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment. Cureus. 15(3). e36727–e36727. 4 indexed citations

Thapa, Resham, Anurag Garikipati, M. Ciobanu, et al.. (2023). Machine Learning Differentiation of Autism Spectrum Sub-Classifications. Journal of Autism and Developmental Disorders. 54(11). 4216–4231. 6 indexed citations

Allen, Angier, et al.. (2022). Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research & Care. 10(1). e002560–e002560. 50 indexed citations

10.

Lam, Carson, et al.. (2022). Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study. JMIR Medical Informatics. 10(6). e36202–e36202. 13 indexed citations

11.

Thapa, Rahul, Anurag Garikipati, Sepideh Shokouhi, et al.. (2022). Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging. 5(2). e35373–e35373. 22 indexed citations

12.

Shokouhi, Sepideh, et al.. (2022). Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients. Thrombosis Research. 216. 14–21. 11 indexed citations

13.

Mataraso, Samson, Gina Barnes, Sepideh Shokouhi, et al.. (2022). Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm. Frontiers in Neurology. 12. 784250–784250. 3 indexed citations

14.

Tso, Chak Foon, Anurag Garikipati, Abigail Green‐Saxena, Qingqing Mao, & Ritankar Das. (2021). Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study. JMIR Public Health and Surveillance. 7(6). e28265–e28265. 19 indexed citations

15.

Lam, Carson, Chak Foon Tso, Abigail Green‐Saxena, et al.. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. JMIR Formative Research. 5(9). e28028–e28028. 13 indexed citations

16.

Thapa, Rahul, Anurag Garikipati, Anna Siefkas, et al.. (2021). Early prediction of severe acute pancreatitis using machine learning. Pancreatology. 22(1). 43–50. 32 indexed citations

17.

Garikipati, Anurag, Nicole S. Zelin, Emily Pellegrini, et al.. (2021). Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data. BioData Mining. 14(1). 23–23. 10 indexed citations

18.

Mao, Qingqing, Melissa Jay, Jana Hoffman, et al.. (2018). Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 8(1). e017833–e017833. 229 indexed citations

19.

Mao, Qingqing, Andreas A. Berlind, Robert J. Scherrer, et al.. (2017). A Cosmic Void Catalog of SDSS DR12 BOSS Galaxies. The Astrophysical Journal. 835(2). 161–161. 34 indexed citations

20.

Calvert, Jacob, Qingqing Mao, Angela J. Rogers, et al.. (2016). A computational approach to mortality prediction of alcohol use disorder inpatients. Computers in Biology and Medicine. 75. 74–79. 15 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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