ResearcherActive in the last 2 years

Salman Ali Khan

ORCID-verified (high confidence)As of 9 November 2026

Matched on ORCID iD 0000-0001-5983-8965; OpenAlex author ID A5054920239, A5138108777.

Their work

Papers: 5 (first author 5, last author 0)
Trials: 0
Grants: 0
Collaborators: 3

Papers, trials and grants by year

2021: 2 papers · 2022: none · 2023: none · 2024: 1 paper · 2025: 1 paper · 2026: 1 paper

Papers 5

Pancreatic Cancer Risk Stratification across Diabetes Stages: Development and Internal Validation of a Machine Learning Model. First author DOI 10.1158/1055-9965.epi-25-1782 ↗
ENDPAC model for defining a high-risk group for pancreatic cancer: Strengths, limitations and the way forward. First author DOI 10.1016/j.pan.2025.11.007 ↗
Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer. First author DOI 10.1097/mcg.0000000000001897 ↗
Validation of the ENDPAC model: Identifying new-onset diabetics at risk of pancreatic cancer. First author DOI 10.1016/j.pan.2021.02.001 ↗
Noninvasive Models Screen New-Onset Diabetics at Low Risk of Early-Onset Pancreatic Cancer. First author DOI 10.1097/mpa.0000000000001917 ↗

Closest collaborators

The 3 strongest of 3 ranked by shared papers, trials and grants, with recent and small-team work weighing most.

Suresh T. Chari · 1 shared paper, 2025
- Paper ENDPAC model for defining a high-risk group for pancreatic cancer: Strengths, limitations and the way forward. DOI 10.1016/j.pan.2025.11.007 ↗
Bharath P Bhushan · 1 shared paper, 2024
- Paper Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer. DOI 10.1097/mcg.0000000000001897 ↗
Justin Thomas Kupec · 2 shared papers, 2021
- Paper Validation of the ENDPAC model: Identifying new-onset diabetics at risk of pancreatic cancer. DOI 10.1016/j.pan.2021.02.001 ↗
- Paper Noninvasive Models Screen New-Onset Diabetics at Low Risk of Early-Onset Pancreatic Cancer. DOI 10.1097/mpa.0000000000001917 ↗

How to reach this person

A chain of introductions through people who have published, run trials or held grants together (the fewest links first, then the links with the most shared work). Built from public records only; no contact details are kept.