Comprehensive Evaluation of CNN, RNN, and CRNN for document retrieval in Healthcare Systems

Document Retrieval (DR) is pivotal in unlocking valuable insights from the ever-growing volume of medical literature. However, precise knowledge extraction from complicated clinical notes, discharge summaries, and research papers is still difficult. However, capturing these distinct dimensions of medical discourse and the complex interdependencies between entities with the currently used approaches becomes difficult. This work solves these shortcomings by introducing a convolutional recurrent neural network (CRNN) framework that merges the advantages of CNNs and RNNs to achieve very high accuracy in medical document extraction tasks. In the CRNN model, the spatial feature extraction capacity of CNNs is integrated with the sequential learning capabilities of RNNs to successfully find medical entities and understand their complex interrelationships, which leads to high-performance document classification. As shown in the case of CORD-19, our CRNN model significantly outperforms individual CNN and RNN models on its entity recognition and relationship extraction tasks (98.93% accuracy), demonstrating that using CRNN in medical documents can yield much better results, thus increasing opportunities for informed clinical decisions, advanced drug discovery, and improved public health interventions.