Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension

doi:10.3748/wjg.v32.i4.113492

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Gastroenterol. Jan 28, 2026; 32(4): 113492
Published online Jan 28, 2026. doi: 10.3748/wjg.v32.i4.113492

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Figure 5 Feature interaction analysis based on SHapley Additive exPlanations. A: Interaction between liver stiffness measurement-to-platelet ratio (LPR) and hemoglobin; B: Interaction between LPR and international normalized ratio; C: Interaction between LPR and spleen thickness; D: Interaction between platelet and spleen thickness. LPR: Liver stiffness measurement-to-platelet ratio; HB: Hemoglobin; INR: International normalized ratio; PLT: Platelet; SHAP: SHapley Additive exPlanations.

Citation: Li YQ, Li ZJ, Li YQ, Feng Y, Wang XB. Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension. World J Gastroenterol 2026; 32(4): 113492
URL: https://www.wjgnet.com/1007-9327/full/v32/i4/113492.htm
DOI: https://dx.doi.org/10.3748/wjg.v32.i4.113492

Li YQ, Li ZJ, Li YQ, Feng Y, Wang XB. Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension. World J Gastroenterol 2026; 32(4): 113492 [PMID: 41640874 DOI: 10.3748/wjg.v32.i4.113492]

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