Chinakorn Sujimongkol

MedicineEconomics, Econometrics and Finance

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 3 papers. Top paper: “Machine Learning to Assess the Prognostic Utility of Radiomic Features for In-hospital COVID-19 Mortality”.

Machine Learning to Assess the Prognostic Utility of Radiomic Features for In-hospital COVID-19 Mortality

Research Square(2022)10.21203/rs.3.rs-2118067/v1

N/A

0DataRank · unranked

0 citations

Stephen Salerno, Xinwei He, Ziyang Pan, Eileen Yang, Chinakorn Sujimongkol +9 more

Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality

Scientific Reports(2023)10.1038/s41598-023-34559-0

N/A

0DataRank · unranked

13 citations

Stephen Salerno, Xinwei He, Ziyang Pan, Eileen Yang, Chinakorn Sujimongkol +9 more

Assessing the Prognostic Utility of Clinical and Radiomic Features for COVID-19 Patients Admitted to ICU: Challenges and Lessons Learned

PubMed(2023)10.1162/99608f92.9d86a749

N/A

0DataRank · unranked

2 citations

Stephen Salerno, Ziyang Pan, Eileen Yang, Chinakorn Sujimongkol, Jiyeon Song +7 more