in this area A New Method for the Task of Automated ICD Coding Using Deep Learning

In order to explore the potential of deep learning for automated ICD coding, this research proposes to develop an end-to-end deep learning approach that combines both natural language processing and supervised learning techniques. Specifically, this approach will use recurrent neural networks to process the clinical text, and it will use Support Vector Machines (SVM) to classify the codes based on the extracted features. The proposed approach will advance the current state-of-the-art in automated ICD coding. In particular, it will define a robust feature engineering process and utilize both transfer learning and deep learning techniques to improve the accuracy and speed of coding. Furthermore, the proposed approach can be used to generate clinically-relevant metadata associated with ICD codes, as well as any additional text-based features found in the medical record. The proposed approach will also leverage recent advancements in natural language processing (NLP) to improve the accuracy of automated ICD coding. Specifically, the proposed approach will combine various NLP techniques such as part-of-speech tagging and semantic role labeling to extract relevant information from the clinical text. Furthermore, it will also take advantage of pre-trained embeddings and recurrent neural networks to capture contextual information. The proposed approach will be tested using a large dataset of clinical text and associated ICD codes. The performance will be evaluated using standard metrics such as recall, precision, and F1 score to compare the results with existing approaches. Furthermore, the model will also be evaluated on its ability to generate clinically-relevant metadata associated with ICD codes. Finally, the proposed approach will be tested for its scalability and robustness. It will be validated on a wide range of clinical datasets from multiple sources such as Electronic Health Records and hospital admission notes. The results of this study will provide insight on how deep learning can be applied to automated ICD coding and the potential of using deep learning for automated ICD coding in the real-world environment.