ICU Length of Stay Prediction for Patients with Diabetes Using Machine Learning and Clinical Notes

Diabetes, a chronic disease, often leads to poor health outcomes and increased healthcare costs, particularly for patients admitted to ICU. Accurate early prediction of ICU length of stay (LOS) is vital for hospital resource management and patient outcome improvement. This study developed predictive models for ICU LOS in diabetic patients by integrating unstructured clinical notes with structured data, including demographics, diagnoses, lab tests, and ICU chart events. Using machine learning techniques, we addressed two tasks: predicting ICU days and classifying stays as long (≥10 days) or short (<10 days). Neural network using Doc2Vec word embedding achieved the best regression performance with an R2 of 0.3626 and mean absolute error of 1.54 days for short stays. For classification, logistic regression with TF-IDF text encoding achieved a best accuracy of 0.875. These results demonstrate the potential of combining structured and unstructured data with machine learning to enhance early ICU LOS predictions, supporting clinical decision-making and resource optimization.