The Application of Deep Learning Tools on Medical Reports to Optimize the Input of an Atrial-Fibrillation-Recurrence Predictive Model

<b>Background</b>: Artificial Intelligence (AI) techniques, particularly Deep Learning (DL) and Natural Language Processing (NLP), have seen exponential growth in the biomedical field. This study focuses on enhancing predictive models for atrial fibrillation (AF) recurrence by extracting valuable data from electronic health records (EHRs) and unstructured medical reports. Although existing models show promise, their reliability is hampered by inaccuracies in coded data, with significant false positives and false negatives impacting their performance. To address this, the authors propose an automated system using DL and NLP techniques to process medical reports, extracting key predictive variables, and identifying new AF cases. The main purpose is to improve dataset reliability so future predictive models can respond more accurately <b>Methods and Results</b>: The study analyzed over one million discharge reports, applying regular expressions and DL tools to extract variables and identify AF onset. The performance of DL models, particularly a feedforward neural network combined with tf-idf, demonstrated high accuracy (0.986) in predicting AF onset. The application of DL tools on unstructured text reduced the error rate in AF identification by 50%, achieving an error rate of less than 2%. <b>Conclusions</b>: This work underscores the potential of AI in optimizing dataset accuracy to develop predictive models and consequently improving the healthcare predictions, offering valuable insights for research groups utilizing secondary data for predictive analytics in this particular setting.