Named Entity Recognition for Medical Records of Heart Failure Using a Pre-trained BERT Model
Mikael Triartama Manurung, I Gusti Ngurah Lanang Wijayakusuma, I Putu Winada Gautama · Journal of Applied Informatics and Computing · 2025
This study aims to develop a Named Entity Recognition (NER) model based on a pre-trained BERT model for medical records of heart failure patients. The focus of this research is to classify essential medical entities from unstructured medical record texts. The classification covers four categories: objective data (patient identity, laboratory test results, and objective examination data), subjective data (patient complaints), prescriptions, and diagnoses (diagnosis codes and descriptions). The methodology employs Natural Language Processing (NLP) techniques using Transformer-based architectures, such as Bidirectional Encoder Representation from Transformers (BERT). The developed model is evaluated based on entity label prediction accuracy and medical entity classification performance. The results indicate that the BERT-based NER model performs well, achieving an entity prediction accuracy of 84.82%. Furthermore, the model effectively classifies medical entities from input texts in alignment with expected medical entities. This research is expected to contribute significantly to medical data management, assist healthcare professionals in clinical decision-making, and serve as a reference for the development of AI-based healthcare technology in Indonesia.