Natural Language Processing and Machine Learning Based Automated Diagnosis of Epilepsy in Electronic Health Record

To promptly obtain an accurate diagnosis to effectively manage and treat epilepsy. This research introduces a novel method for automating the epilepsy diagnostic process in EHRs using Natural Language Processing (NLP) and Machine Learning (ML). Therefore, natural language processing (NLP) techniques may be employed to extract medically relevant information from both structured data contained in electronic health records (EHRs) and unstructured clinical notes. Next, machine learning techniques will be employed to predict the presence or absence of epilepsy. The approach's high overall accuracy, specificity, and sensitivity, as assessed on a comprehensive dataset, make it a promising candidate for clinical applications. This diagnostic automation strategy is designed to achieve a more precise diagnosis, reduced anxiety for physicians, and improved health outcomes for patients. The results illustrate the practicality and utility of incorporating advanced data analysis techniques into clinical procedures, and the technology shows potential for the detection of neurological disorders such as epilepsy.