Machine Learning Enhanced Acute Heart Failure Phenotype Prediction using Natural Language Processing and Random Forest

Pei-Hsuan Chang¹Feng-Ching Liao¹Yi-Ching Wu¹Fang-Ju Sun¹Yen-Yu Liu²Hung-I Yeh²Chung-Lieh Hung^3*Kun-Pin Wu¹

• ¹National Yang Ming Chiao Tung University, Taipei, Taiwan
• ²Mackay Memorial Hospital, Taipei City, Taiwan
• ³Mackay Medical College, New Taipei, Taiwan

ABSTRACT Background Heart failure (HF), with its distinct phenotypes, poses significant public health challenges. Early diagnosis of specific HF phenotypes is crucial for timely therapeutic intervention. Objectives We employed random forests to predict acute HF (AHF) phenotypes (HFrEF, HFmrEF, and HFpEF) during admission, using structured and unstructured data types while blinded to left ventricular ejection fraction (LVEF) information. Methods We investigated the predictive performance of integrated natural language processing (NLP) and machine learning (ML)-based models in AHF phenotype classification by random forests, leveraging clinical text and laboratory data from the MIMIC-III database. Feature selection for unstructured textual data and biochemical test data was performed using the LASSO method, with selected textual features converted into structured data using one-hot encoding. The areas under the ROC and PRC curves (AUROC and AUPRC) assessed overall performance. Results Our final study cohort comprised 1,192 training datasets and 513 independent validating datasets with primary data types and LVEF information available. The overall model from the training dataset showed the best performance with combined datasets (accuracy: 0.70±0.03, AUROC: 0.76±0.02) compared to the textual or laboratory dataset alone, which was replicated in the independent validating dataset. Our model achieved optimal performance by selecting up to 100 combined features from both textual and laboratory data. Reducing features to 20 did not substantially attenuate the overall model performance until only 10 features were selected. Conclusions Our study enhances HF phenotype classification and underscores the value of multifaceted data analysis in clinical informatics, enabling more personalized heart failure treatment. Early identification of AHF phenotypes may support timely, phenotype-specific management and inform treatment decisions.