Skip to content

Author

J. Chhatwal

We have 4 of 259 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

Recurrence detection in patients with triple-negative breast cancer following the current standard of care: a microsimulation model

Abstract Objectives Breast cancer is the most common cancer among women in the USA, accounting for approximately 31% of new cases and 15% of cancer-related deaths in females. Triple-negative breast cancer (TNBC) is the most lethal subtype with 29 months median overall survival. Despite curative-intent surgery and syste...

S. Samur, O. Hoban, N. Gu et al. · 0 citations
Review Sep 2026

Generative Artificial Intelligence for Systematic Literature Reviews: A Good Practices Report of an ISPOR Special Task Force.

OBJECTIVES Systematic literature reviews (SLRs) are foundational to evidence-based medicine, including health technology assessment (HTA) and health economics and outcomes research (HEOR). Generative artificial intelligence (GenAI) tools are increasingly used in SLR workflows, yet no good practice guidance exists. This...

R. Fleurence, Riaz Qureshi, Rakesh Aggarwal et al. · 0 citations
Open access Jul 2026

Small-Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection

A Data-Driven Early Warning System for Disease Outbreaks Early detection of infectious disease outbreaks is essential for timely public health response, yet local case data are often sparse and noisy, making reliable monitoring difficult. In their paper, “Small-Area Estimation of Case Growths for Timely COVID-19 Outbr...

Zhaowei She, Zilong Wang, Turgay Ayer et al. · 0 citations
Review Jul 2026

The Use of Generative Artificial Intelligence in Systematic Literature Reviews: A Rapid Review of the Literature.

GenAI can improve efficiency across multiple SLR tasks when used in hybrid human-AI workflows when used in hybrid human-AI workflows, and current evidence supports targeted, task-specific adoption with transparent reporting and human oversight, rather than full automation.

R. Fleurence, Riaz Qureshi, Rakesh Aggarwal et al. · 1 citation

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.