Publication
Predicting disease progression and mortality in prostate cancer using real-world data-driven time-inhomogeneous Markov models.
Jiaqi Wang, Yuanshi Jiao, Dawn Craig, Steven Wai Kwan Siu, Lei Si, David Makram Bishai, Yi Yang, Yingyao Chen, Qingpeng Zhang, Rong Na, Xue Li
- Source
- PubMed
- Retrieved
- Sep 29, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-PUBMED-20260929-000001
Abstract
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Prostate cancer progression varies across patients. Understanding disease trajectories and mortality risk is important for improved healthcare. This study developed a time-inhomogeneous Markov model using territory-wide electronic medical records from Hong Kong to estimate 10-year disease…
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Candidate 1
- cancerMalignant Prostate Neoplasmdictionary0.60
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