Research map: Application of a machine learning model integrating T cell subsets and clinical markers in 28-Day mortality prediction of sepsis patients

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  1. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study · Kristina E. Rudd · 2020 · 8209 citations · Cited by this paper
  2. LRP8 drives cervical cancer lymph node metastasis by promoting epithelial-mesenchymal transition and VEGF-C secretion · 2026 · Related
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  5. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021 · Laura Evans · 2021 · 2971 citations · Cited by this paper
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  9. The immunopathology of sepsis and potential therapeutic targets · Tom van der Poll · 2017 · 2012 citations · Cited by this paper
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  11. Plasticity of CD4+ T Cell Lineage Differentiation · Liang Zhou · 2009 · 1560 citations · Cited by this paper
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  14. Population-specific association and risk discrimination utility of the cardiometabolic index for prevalent hypertension–diabetes comorbidity: insights from CHARLS and NHANES · 2026 · Related
  15. Effect of a Resuscitation Strategy Targeting Peripheral Perfusion Status vs Serum Lactate Levels on 28-Day Mortality Among Patients With Septic Shock · Glenn Hernández · 2019 · 949 citations · Cited by this paper
  16. Beyond polarization: a receptor-centered framework for macrophage function and therapy in skin diseases · 2026 · Related
  17. Biomarkers of sepsis: time for a reappraisal · Charalampos N. Pierrakos · 2020 · 736 citations · Cited by this paper
  18. Dissecting PCD-driven molecular landscapes in AML: a multi-omic framework for prognostication and therapeutic targeting · 2026 · Related
  19. CD8+ T cell metabolism in infection and cancer · Miguel Reina‐Campos · 2021 · 634 citations · Cited by this paper
  20. Thromboembolic risk in subcutaneous versus intravenous immunoglobulin therapy: a systematic review and narrative meta-synthesis · 2026 · Related
  21. An immune-cell signature of bacterial sepsis · Miguel Reyes · 2020 · 541 citations · Cited by this paper
  22. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey · Jianfeng Xie · 2019 · 526 citations · Cited by this paper
  23. Origins of CD4+ effector and central memory T cells · Marion Pepper · 2011 · 413 citations · Cited by this paper
  24. Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician · Anastasia A Theodosiou · 2023 · 243 citations · Cited by this paper
  25. Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications · Paul Thottakkara · 2016 · 205 citations · Cited by this paper
  26. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU · Guilan Kong · 2020 · 184 citations · Cited by this paper
  27. Biomarker-Guided Antibiotic Duration for Hospitalized Patients With Suspected Sepsis · Paul M Dark · 2024 · 105 citations · Cited by this paper
  28. Sustained and Incomplete Recovery of Naive CD8+ T Cell Precursors after Sepsis Contributes to Impaired CD8+ T Cell Responses to Infection · Stephanie A. Condotta · 2013 · 92 citations · Cited by this paper
  29. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning · Fei Tian · 2024 · 76 citations · Cited by this paper
  30. The relationship between hemoglobin, albumin, lymphocyte, and platelet (HALP) score and 28-day mortality in patients with sepsis: a retrospective analysis of the MIMIC-IV database · Huan Li · 2025 · 48 citations · Cited by this paper
  31. Revolution in sepsis: a symptoms-based to a systems-based approach? · Geoffrey Phillip Dobson · 2024 · 25 citations · Cited by this paper
  32. Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis · Weiqiang Liao · 2023 · 14 citations · Cited by this paper
  33. Evidence for Monocyte Reprogramming in a Long-Term Postsepsis Study · Raquel Bragante Gritte · 2022 · 9 citations · Cited by this paper
  34. Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis · Ye Zhang · 2025 · 7 citations · Cited by this paper
  35. The tissue factor expression on CD14++CD16-monocytes is a new markers in the Chinese Han older adult population with sepsis: A prospective study · Qian Gao · 2022 · 3 citations · Cited by this paper
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