Aaron J. Masino

1.9k total citations
44 papers, 801 citations indexed

About

Aaron J. Masino is a scholar working on Artificial Intelligence, Public Health, Environmental and Occupational Health and Epidemiology. According to data from OpenAlex, Aaron J. Masino has authored 44 papers receiving a total of 801 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Public Health, Environmental and Occupational Health and 7 papers in Epidemiology. Recurrent topics in Aaron J. Masino's work include Biomedical Text Mining and Ontologies (6 papers), Topic Modeling (5 papers) and Neonatal and Maternal Infections (5 papers). Aaron J. Masino is often cited by papers focused on Biomedical Text Mining and Ontologies (6 papers), Topic Modeling (5 papers) and Neonatal and Maternal Infections (5 papers). Aaron J. Masino collaborates with scholars based in United States, Australia and Argentina. Aaron J. Masino's co-authors include Anne Cocos, Alexander G. Fiks, Robert W. Grundmeier, Félice Lê‐Scherban, Xueqin Pang, Christopher B. Forrest, Mary Catherine Harris, Svetlana Ostapenko, Lakshmi Srinivasan and Christopher P. Bonafide and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Anesthesiology.

In The Last Decade

Aaron J. Masino

44 papers receiving 780 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aaron J. Masino United States 15 201 156 147 130 85 44 801
Todd Lingren United States 19 415 2.1× 132 0.8× 162 1.1× 393 3.0× 239 2.8× 29 1.2k
Adler Perotte United States 15 297 1.5× 137 0.9× 111 0.8× 239 1.8× 136 1.6× 36 1.2k
Suzanne Tamang United States 14 492 2.4× 146 0.9× 186 1.3× 143 1.1× 166 2.0× 66 1.4k
Phung‐Anh Nguyen Taiwan 16 147 0.7× 174 1.1× 55 0.4× 64 0.5× 163 1.9× 47 945
Yiye Zhang United States 15 183 0.9× 96 0.6× 180 1.2× 88 0.7× 127 1.5× 67 983
Vivienne J. Zhu United States 13 97 0.5× 148 0.9× 83 0.6× 104 0.8× 90 1.1× 24 784
Kayo Waki Japan 21 105 0.5× 226 1.4× 207 1.4× 168 1.3× 48 0.6× 72 1.5k
Theresa A. Koleck United States 16 215 1.1× 75 0.5× 70 0.5× 152 1.2× 86 1.0× 41 898
Rupa Makadia United States 7 123 0.6× 62 0.4× 69 0.5× 116 0.9× 69 0.8× 12 598
Antoine Neuraz France 19 185 0.9× 133 0.9× 55 0.4× 381 2.9× 74 0.9× 63 1.2k

Countries citing papers authored by Aaron J. Masino

Since Specialization
Citations

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

Fields of papers citing papers by Aaron J. Masino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aaron J. Masino. 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 Aaron J. Masino. The network helps show where Aaron J. Masino may publish in the future.

Co-authorship network of co-authors of Aaron J. Masino

This figure shows the co-authorship network connecting the top 25 collaborators of Aaron J. Masino. A scholar is included among the top collaborators of Aaron J. Masino 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 Aaron J. Masino. Aaron J. Masino 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.
Fiol, Guilherme Del, Kimberly A. Kaphingst, Jackilen Shannon, et al.. (2025). Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: Prompt Engineering Project. JMIR Cancer. 11. e65848–e65848. 1 indexed citations
2.
Masino, Aaron J., Mary Catherine Harris, Lyle Ungar, et al.. (2025). Aligning prediction models with clinical information needs: infant sepsis case study. JAMIA Open. 8(2). ooaf015–ooaf015. 1 indexed citations
3.
Herrington, John D., Aaron J. Masino, Xueqin Pang, et al.. (2024). Physiological and communicative emotional disconcordance in children on the autism spectrum. Journal of Neurodevelopmental Disorders. 16(1). 51–51. 2 indexed citations
4.
Ramachandran, Ravi P., et al.. (2023). Revisiting the fragility of influence functions. Neural Networks. 162. 581–588. 1 indexed citations
5.
Kark, Sarah M., et al.. (2023). Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management. Frontiers in Digital Health. 5. 1221754–1221754. 1 indexed citations
6.
Campbell, Elizabeth A., Mitchell Maltenfort, Justine Shults, Christopher B. Forrest, & Aaron J. Masino. (2022). Characterizing clinical pediatric obesity subtypes using electronic health record data. SHILAP Revista de lepidopterología. 1(8). e0000073–e0000073. 1 indexed citations
7.
Nuske, Heather J., Matthew S. Goodwin, Jeffrey W. Pennington, et al.. (2021). Evaluating commercially available wireless cardiovascular monitors for measuring and transmitting real‐time physiological responses in children with autism. Autism Research. 15(1). 117–130. 15 indexed citations
8.
Pang, Xueqin, Christopher B. Forrest, Félice Lê‐Scherban, & Aaron J. Masino. (2021). Prediction of early childhood obesity with machine learning and electronic health record data. International Journal of Medical Informatics. 150. 104454–104454. 67 indexed citations
9.
Kenyon, Chén C., et al.. (2021). Personalized prediction of early childhood asthma persistence: A machine learning approach. PLoS ONE. 16(3). e0247784–e0247784. 35 indexed citations
10.
Folweiler, Kaitlin A., Danielle K. Sandsmark, Ramon Diaz‐Arrastia, Akiva S. Cohen, & Aaron J. Masino. (2020). Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes. Journal of Neurotrauma. 37(12). 1431–1444. 29 indexed citations
11.
Simpao, Allan F., Lezhou Wu, Jorge A. Gálvez, et al.. (2020). Preoperative Fluid Fasting Times and Postinduction Low Blood Pressure in Children. Anesthesiology. 133(3). 523–533. 39 indexed citations
12.
Jiang, Shen, Akira Nishisaki, Amanda Nickel, et al.. (2020). Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry. Frontiers in Physiology. 11. 564589–564589. 5 indexed citations
13.
Masino, Aaron J., et al.. (2019). A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine. Journal of Cardiothoracic and Vascular Anesthesia. 34(2). 479–482. 9 indexed citations
14.
Gálvez, Jorge A., Lezhou Wu, Allan F. Simpao, et al.. (2019). Duration of preoperative clear fluid fasting and peripheral intravenous catheterization in children: A single‐center observational cohort study of 9693 patients. Pediatric Anesthesia. 30(2). 137–146. 6 indexed citations
15.
Srinivasan, Lakshmi, Robert W. Grundmeier, Okan U. Elci, et al.. (2019). Surviving Sepsis in a Referral Neonatal Intensive Care Unit: Association between Time to Antibiotic Administration and In-Hospital Outcomes. The Journal of Pediatrics. 217. 59–65.e1. 42 indexed citations
16.
Masino, Aaron J., et al.. (2018). Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features. PubMed. 2(1-2). 25–43. 12 indexed citations
17.
Cocos, Anne & Aaron J. Masino. (2017). Combining rule-based and neural network systems for extracting adverse reactions from drug labels.. Theory and applications of categories. 2 indexed citations
18.
Cocos, Anne, Ting Qian, Chris Callison-Burch, & Aaron J. Masino. (2017). Crowd control: Effectively utilizing unscreened crowd workers for biomedical data annotation. Journal of Biomedical Informatics. 69. 86–92. 22 indexed citations
19.
Masino, Aaron J., Robert W. Grundmeier, Jeffrey W. Pennington, John A. Germiller, & E. Bryan Crenshaw. (2016). Temporal bone radiology report classification using open source machine learning and natural langue processing libraries. BMC Medical Informatics and Decision Making. 16(1). 65–65. 22 indexed citations
20.
Cocos, Anne, Aaron J. Masino, Ting Qian, Ellie Pavlick, & Chris Callison-Burch. (2015). Effectively Crowdsourcing Radiology Report Annotations. 109–114. 5 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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