Affiliations: [a] Department of Computer Science and Engineering, SVNIT, Surat, Gujarat, India | [b] Department of Computer Engineering, SVNIT University, Surat, Gujarat, India
Correspondence:
[*]
Corresponding auathor: Pranita Mahajan, Department of Computer Science and Engineering, SVNIT, Surat, Gujarat, India. Tel.: +91 9920188940; E-mail: Pranita.mahajan@gmail.com.
Abstract: Electronic Medical Records (EMR) carry important information about a patient’s journey. The past decade shows substantial use of Natural Language Processing (NLP)-based Information Retrieval (IR) techniques to extract insights such as symptoms, diseases, and tests from these unstructured records. The state-of-the-art shows that convolutional neural networks (CNN) make a significant contribution to the disease classification task.A significant improvement in precise knowledge mining is possible with precise feature extraction. Feature selection addresses undesirable, unneeded, or irrelevant features. This article proposes a Modified Rider Optimization Algorithm (MROA) to choose important features by selecting optimal weights from a pool of randomly generated weights based on high accuracy and less training time in the CNN algorithm. A modified approach is trained on 114 N2C2 patients’ records to extract symptoms, disease, and tests are performed on them to perform disease classification tasks. The proposed approach is found to be accurate, with 97.77% accuracy in the disease classification and treatment prediction task from EMR.
Keywords: CNN optimization, electronic medical records, deep learning, information extraction, cBERT word embedding, disease prediction
