Early prediction of thrombocytopenia in critical ill patients admitted to the intensive care unit based on sequence embedding

Thrombocytopenia is a common complication among critically ill patients. To enable early prediction, we conducted a retrospective study using five machine learning (ML) models developed with a sequence embedding approach that integrates temporal medication and diagnostic data. Models were trained on the MIMIC‐IV database and evaluated on the eICU database. We propose a novel sequence feature fusion method combining explicit and implicit features with embeddings for ICD codes and drug sequences to capture complex interactions. To our knowledge, this is the first study to make continuous predictions for ICU patients until thrombocytopenia onset. Model performance was assessed using AUC; t‐SNE and SHAP were used to evaluate feature importance. XGBoost with sequence feature fusion performed best, achieving AUCs of 0.80, 0.85, and 0.92 at ICU admission, and 72 h and 24 h before onset, respectively. Platelet count, phosphate, and lactate were the top predictors. These findings demonstrate that ML models with sequence embeddings can effectively predict thrombocytopenia by capturing temporal patterns in patient data.