Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish

Introduction: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to inconsistent and not mandatory International Classification of Diseases (ICD) codes. We developed and validated Natural Language Processing (NLP) and machine learning models to classify the status of kidney allografts in unstructured text in EMRs written in Spanish. Methods: We conducted a retrospective cohort of 2712 patients transplanted between July 2008 and January 2023, analyzing 117,566 unstructured medical records. NLP involved text normalization, tokenization, stopwords removal, spell-checking, elimination of low-frequency words and stemming. Data was split in training, validation and test sets. Data balance was performed using undersampling technique. Feature selection was performed using LASSO regression. We developed, validated and tested Logistic Regression, Random Forest, and Neural Networks models using 10-fold cross-validation. Performance metrics included area under the curve, F1 Score, accuracy, sensitivity, specificity, Negative Predictive Value, and Positive Predictive Value. Results: The test performance results showed that the Random Forest model achieved the highest AUC (0.98) and F1 score (0.65). However, it had a modest sensitivity (0.76) and a relatively low PPV (0.56), implying a significant number of false positives. The Neural Network model also performed well with a high AUC (0.98) and reasonable F1 score (0.61), but its PPV (0.49) was lower, indicating more false positives. The Logistic Regression model, while having the lowest AUC (0.91) and F1 score (0.49), showed the highest sensitivity (0.83) with the lowest PPV (0.35). Conclusion: We developed and validated three machine learning models combined with NLP techniques for unstructured texts written in Spanish. The models performed well on the validation set but showed modest performance on the test set due to data imbalance. These models could be adapted for clinical practice, though they may require additional manual work due to high false positive rates.