Mohammad M. Ghassemi

10.0k total citations · 1 hit paper
66 papers, 6.0k citations indexed

About

Mohammad M. Ghassemi is a scholar working on Artificial Intelligence, Epidemiology and Emergency Medicine. According to data from OpenAlex, Mohammad M. Ghassemi has authored 66 papers receiving a total of 6.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 12 papers in Epidemiology and 12 papers in Emergency Medicine. Recurrent topics in Mohammad M. Ghassemi's work include Cardiac Arrest and Resuscitation (11 papers), Machine Learning in Healthcare (8 papers) and Traumatic Brain Injury and Neurovascular Disturbances (8 papers). Mohammad M. Ghassemi is often cited by papers focused on Cardiac Arrest and Resuscitation (11 papers), Machine Learning in Healthcare (8 papers) and Traumatic Brain Injury and Neurovascular Disturbances (8 papers). Mohammad M. Ghassemi collaborates with scholars based in United States, Netherlands and Belgium. Mohammad M. Ghassemi's co-authors include Benjamin Moody, Leo Anthony Celi, Roger G. Mark, Mengling Feng, Tom Pollard, Alistair E. W. Johnson, Peter Szolovits, Shamim Nemati, Gari D. Clifford and Gregory T. Spear and has published in prestigious journals such as The Journal of Experimental Medicine, The Journal of Immunology and PLoS ONE.

In The Last Decade

Mohammad M. Ghassemi

59 papers receiving 5.8k citations

Hit Papers

MIMIC-III, a freely accessible critical care database 2016 2026 2019 2022 2016 1000 2.0k 3.0k 4.0k
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.

  1. MIMIC-III, a freely accessible critical care database
    2016 4.7k citations Alistair E. W. Johnson, Mengling Feng et al. profile →

Peers

Mohammad M. Ghassemi
Mengling Feng Singapore
Benjamin Moody United States
Tom Pollard United States
Benjamin S. Glicksberg United States
Alistair E. W. Johnson United States
Tianxi Cai United States
Azra Bihorac United States
Andrew L. Beam United States
Peter R. Rijnbeek Netherlands
Riccardo Miotto United States
Mengling Feng Singapore
Mohammad M. Ghassemi
Citations per year, relative to Mohammad M. Ghassemi Mohammad M. Ghassemi (= 1×) peers Mengling Feng

Countries citing papers authored by Mohammad M. Ghassemi

Since Specialization
Citations

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

Fields of papers citing papers by Mohammad M. Ghassemi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammad M. Ghassemi. 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 Mohammad M. Ghassemi. The network helps show where Mohammad M. Ghassemi may publish in the future.

Co-authorship network of co-authors of Mohammad M. Ghassemi

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad M. Ghassemi. A scholar is included among the top collaborators of Mohammad M. Ghassemi 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 Mohammad M. Ghassemi. Mohammad M. Ghassemi 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.
Guha, Avirup, Fei Fang, Qianyi Wang, et al.. (2025). AI-driven prediction of cardio-oncology biomarkers through protein corona analysis. Chemical Engineering Journal. 509. 161134–161134. 3 indexed citations
2.
Thind, Kundan, et al.. (2025). Calibrating LLM Confidence by Probing Perturbed Representation Stability. 10459–10525.
3.
Ghanem, A.I., Kyle Verdecchia, R. Hall, et al.. (2024). A Novel Localized Student-Teacher LLM for Enhanced Toxicity Extraction in Radiation Oncology. International Journal of Radiation Oncology*Biology*Physics. 120(2). e632–e633.
4.
Andersen, Kathleen M., Farid Khan, Peter W. Park, et al.. (2024). Using a data-driven approach to define post-COVID conditions in US electronic health record data. PLoS ONE. 19(4). e0300570–e0300570. 1 indexed citations
5.
Bagher‐Ebadian, Hassan, Stephen L. Brown, Mohammad M. Ghassemi, et al.. (2023). Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models. Scientific Reports. 13(1). 9672–9672. 5 indexed citations
6.
Reyna, Matthew A., Edilberto Amorim, Reza Sameni, et al.. (2023). Predicting Neurological Recovery from Coma after Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. Computing in cardiology. 50. 4 indexed citations
7.
Amorim, Edilberto, Wei‐Long Zheng, Jin Jing, et al.. (2023). Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest. Neurology. 101(9). e940–e952. 3 indexed citations
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Bagher‐Ebadian, Hassan, Stephen L. Brown, Mohammad M. Ghassemi, et al.. (2023). Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Scientific Reports. 13(1). 10693–10693. 3 indexed citations
11.
Ghassemi, Mohammad M., Chunli Song, & Tuka Alhanai. (2020). The Automated Venture Capitalist: Data and Methods to Predict the Fate of Startup Ventures. National Conference on Artificial Intelligence. 2 indexed citations
12.
Amorim, Edilberto, Shirley Mo, Sebastian Palacios, et al.. (2020). Cost-effectiveness analysis of multimodal prognostication in cardiac arrest with EEG monitoring. Neurology. 95(5). e563–e575. 3 indexed citations
13.
Lokhandwala, Sharukh, Abdullah Chahin, Mengling Feng, et al.. (2018). One-year mortality after recovery from critical illness: A retrospective cohort study. PLoS ONE. 13(5). e0197226–e0197226. 13 indexed citations
14.
Dernoncourt, Franck, Mohammad M. Ghassemi, & Walter Chang. (2018). A Repository of Corpora for Summarization. Language Resources and Evaluation. 20 indexed citations
15.
Nemati, Shamim, Mohammad M. Ghassemi, Nino Isakadze, et al.. (2016). Monitoring and detecting atrial fibrillation using wearable technology. PubMed. 2016. 3394–3397. 112 indexed citations
16.
Johnson, Alistair E. W., Mohammad M. Ghassemi, Shamim Nemati, et al.. (2016). Machine Learning and Decision Support in Critical Care. Proceedings of the IEEE. 104(2). 444–466. 5 indexed citations
17.
Amorim, Edilberto, Sandipan Pati, Patrick L. Purdon, et al.. (2015). An enhanced cerebral recovery index for coma prognostication following cardiac arrest. DSpace@MIT (Massachusetts Institute of Technology). 3 indexed citations
18.
Ghassemi, Mohammad M., et al.. (2014). A data-driven approach to optimized medication dosing: a focus on heparin. Intensive Care Medicine. 40(9). 1332–1339. 41 indexed citations
19.
Liu, Jingyu, Mohammad M. Ghassemi, Andrew M. Michael, et al.. (2012). An ICA with reference approach in identification of genetic variation and associated brain networks. Frontiers in Human Neuroscience. 6. 21–21. 23 indexed citations
20.
Saifuddin, Mohammed, Charles J. Parker, Mark E. Peeples, et al.. (1995). Role of virion-associated glycosylphosphatidylinositol-linked proteins CD55 and CD59 in complement resistance of cell line-derived and primary isolates of HIV-1.. The Journal of Experimental Medicine. 182(2). 501–509. 201 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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