Nathan Radakovich

496 total citations
15 papers, 294 citations indexed

About

Nathan Radakovich is a scholar working on Hematology, Genetics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nathan Radakovich has authored 15 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Hematology, 6 papers in Genetics and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nathan Radakovich's work include Acute Myeloid Leukemia Research (7 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (5 papers) and Digital Imaging for Blood Diseases (3 papers). Nathan Radakovich is often cited by papers focused on Acute Myeloid Leukemia Research (7 papers), Myeloproliferative Neoplasms: Diagnosis and Treatment (5 papers) and Digital Imaging for Blood Diseases (3 papers). Nathan Radakovich collaborates with scholars based in United States, Germany and Italy. Nathan Radakovich's co-authors include Aziz Nazha, Matthew Nagy, Jacob Shreve, Hamid Borghei‐Razavi, Lee Hwang, Krishna C. Joshi, Matthew M. Grabowski, Alireza M. Mohammadi, Jessica S. Ancker and Curtis J. Donskey and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Clinical Infectious Diseases.

In The Last Decade

Nathan Radakovich

15 papers receiving 288 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nathan Radakovich United States	9	82	62	60	52	44	15	294
Ibrahim N. Muhsen United States	10	59 0.7×	63 1.0×	42 0.7×	22 0.4×	15 0.3×	41	292
Sepideh Shakeri Iran	14	118 1.4×	57 0.9×	300 5.0×	77 1.5×	22 0.5×	42	717
Liyan Pan China	8	90 1.1×	32 0.5×	96 1.6×	12 0.2×	32 0.7×	18	279
Giovanni Maria Rossi Italy	11	75 0.9×	32 0.5×	43 0.7×	13 0.3×	17 0.4×	38	530
Tarek Owaidah Saudi Arabia	11	46 0.6×	53 0.9×	29 0.5×	59 1.1×	6 0.1×	54	446
Franco Verde United States	14	31 0.4×	25 0.4×	85 1.4×	12 0.2×	8 0.2×	27	528
Nataliya Kovalchuk United States	13	23 0.3×	28 0.5×	275 4.6×	12 0.2×	18 0.4×	64	484
Szu‐Hee Lee Australia	10	44 0.5×	75 1.2×	12 0.2×	75 1.4×	7 0.2×	20	329
Shiue‐Wei Lai Taiwan	10	30 0.4×	35 0.6×	13 0.2×	36 0.7×	3 0.1×	30	274
Stephen Hobbs United States	11	112 1.4×	46 0.7×	218 3.6×	4 0.1×	42 1.0×	28	783

Countries citing papers authored by Nathan Radakovich

Since Specialization

Citations

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

Fields of papers citing papers by Nathan Radakovich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Radakovich

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

All Works

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15 of 15 papers shown

Radakovich, Nathan, David A. Sallman, Rena Buckstein, et al.. (2022). A machine learning model of response to hypomethylating agents in myelodysplastic syndromes. iScience. 25(10). 104931–104931. 10 indexed citations

Jain, Vardhmaan, Agam Bansal, Nathan Radakovich, et al.. (2021). Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study. Journal of Cardiothoracic and Vascular Anesthesia. 35(7). 2063–2069. 23 indexed citations

Shreve, Jacob, et al.. (2021). Personalized Prediction of Hospital Mortality in COVID-19–Positive Patients. SHILAP Revista de lepidopterología. 5(4). 795–801. 8 indexed citations

Radakovich, Nathan, Luca Malcovati, Manja Meggendorfer, et al.. (2020). Genotype-Phenotype Correlations in Patients with Myeloid Malignancies Using Explainable Artificial Intelligence. Blood. 136(Supplement 1). 31–32. 2 indexed citations

Radakovich, Nathan, Matthew Nagy, & Aziz Nazha. (2020). Machine learning in haematological malignancies. The Lancet Haematology. 7(7). e541–e550. 71 indexed citations

Radakovich, Nathan, Matthew Nagy, & Aziz Nazha. (2020). Artificial Intelligence in Hematology: Current Challenges and Opportunities. Current Hematologic Malignancy Reports. 15(3). 203–210. 33 indexed citations

Radakovich, Nathan, Jacob Shreve, Sudipto Mukherjee, et al.. (2020). Personalized Transcriptomic Analyses Identify Unique Signatures That Correlate with Genomic Subtypes in Acute Myeloid Leukemia (AML) Using Explainable Artificial Intelligence. Blood. 136(Supplement 1). 33–34. 2 indexed citations

Radakovich, Nathan, et al.. (2020). 778P Concomitant antibiotic use and its effect on immune-checkpoint inhibitor efficacy in patients with advanced urothelial carcinoma. Annals of Oncology. 31. S597–S597. 4 indexed citations

Radakovich, Nathan, Matthew M. Grabowski, Hamid Borghei‐Razavi, et al.. (2020). Lessons Learned in Using Laser Interstitial Thermal Therapy for Treatment of Brain Tumors: A Case Series of 238 Patients from a Single Institution. World Neurosurgery. 139. e345–e354. 33 indexed citations

10.

Radakovich, Nathan, et al.. (2020). The Impact of Clinical Decision Support Alerts onClostridioides difficileTesting: A Systematic Review. Clinical Infectious Diseases. 72(6). 987–994. 16 indexed citations

11.

Radakovich, Nathan, et al.. (2020). Acute myeloid leukemia and artificial intelligence, algorithms and new scores. Best Practice & Research Clinical Haematology. 33(3). 101192–101192. 21 indexed citations

12.

Radakovich, Nathan, Manja Meggendorfer, Luca Malcovati, et al.. (2020). A Personalized Clinical-Decision Tool to Improve the Diagnostic Accuracy of Myelodysplastic Syndromes. Blood. 136(Supplement 1). 33–35. 2 indexed citations

13.

Nagy, Matthew, Nathan Radakovich, & Aziz Nazha. (2020). Machine Learning in Oncology: What Should Clinicians Know?. JCO Clinical Cancer Informatics. 4(4). 799–810. 50 indexed citations

14.

Shreve, Jacob, Manja Meggendorfer, Hassan Awada, et al.. (2019). A Personalized Prediction Model to Risk Stratify Patients with Acute Myeloid Leukemia (AML) Using Artificial Intelligence. Blood. 134(Supplement_1). 2091–2091. 15 indexed citations

15.

Radakovich, Nathan, Mikkael A. Sekeres, Sudipto Mukherjee, et al.. (2019). Predicting Response to Hypomethylating Agents in Patients with Myelodysplastic Syndromes (MDS) Using Artificial Intelligence (AI). Blood. 134(Supplement_1). 2089–2089. 4 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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