Pengembangan Sistem Klasifikasi Diagnosa Medis Menggunakan Progressive Web Application Terintegrasi Machine Learning - Muhammad Andika Fadilla; M Fakhri Sholahuddin; Tata Sutabri (Jurnal Syntax Admiration, 2024)
This research aims to present a solution that not only improves efficiency in coding medical diagnoses, but also ensures high accuracy at various levels of case complexity. The classification of medical diagnoses is a crucial aspect of health information management, particularly in coding diagnoses into the ICD-10 and ICD-9CM standards. A Progressive Web Application (PWA) has been developed to automate this process, leveraging Machine Learning technology with the Sentence Transformer model architecture "paraphrase-multilingual-mpnet-base-v2." The application development follows a prototyping method, enabling an iterative process focused on user needs and delivering an effective and efficient solution. The application incorporates key PWA features such as offline access, push notifications, and installation as a standalone application on user devices. The backend is built using Flask to handle medical diagnosis input and generate classification predictions through integration with the machine learning model. Meanwhile, the frontend utilizes React.js to provide a responsive and user-friendly interface. Testing results indicate that the application delivers highly accurate diagnosis classifications, even for complex cases. With its features and capabilities, this application has the potential to enhance the efficiency and accuracy of medical diagnosis coding processes in healthcare facilities.