Machine Learning for Anaphylaxis Prediction, Tailored Medicine Prescription, Eczema Detection, and Privacy-Preserving Medical Data Analysis through User Friendly Chatbot
Keywords:Machine Learning, CNN, Natural Language Processing (NLP), Encryption, Anaphylaxis, LSTM, GPT-2, Healthcare Technology
The integration of cutting-edge technology into healthcare diagnosis and data management remains pivotal in modern medicine. This compilation synthesizes four prominent studies, all exploring diverse applications of technological advancements in medical scenarios. Firstly, a CNN-based machine learning model has been devised to swiftly diagnose anaphylaxis and recommend adrenaline administration. Secondly, a unique framework employing natural language processing (NLP) combined with deep learning scrutinizes skin images for allergy detection, aiming at elevating healthcare standards in Sri Lanka. Thirdly, addressing the paramount issue of medical data privacy in electronic health records, an integrated architecture is proposed, amalgamating advanced encryption techniques with fine-grained access control. Lastly, a comprehensive comparison between LSTM networks and GPT-2 language transformers is undertaken, emphasizing their efficacy in medical chatbot systems, particularly for analyzing intricate patient histories like anaphylaxis. Collectively, these studies underscore the transformative potential of technological interventions, striving for accuracy, efficiency, and privacy in healthcare.
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Copyright (c) 2023 H. W. D. S. W. K. K. Dissanayake, Nuwanga Wijamuni, S. D. Muthuni Suwanthi, L. M. Dilanka Matheesha Rajapakse, H. M. Samadhi Chathuranga Rathnayake, Sanjeevi Chandrasiri
This work is licensed under a Creative Commons Attribution 4.0 International License.