A Method and Practice for Menopausal Disease Prediction Based on Knowledge Graph
2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)(2023)
摘要
As the population of women entering menopause grows, so does the potential for chronic conditions like os-teoporosis, cardiovascular disease, and metabolic syndrome, which underscores the the critical need of early intervention and treatment during menopause. Meanwhile the Electronic Healthcare Records (EHR) is becoming a valuable resource for health event predictions, including diagnosis, mortality, length-of-stay, and readmission. However, effectively utilizing diagnosis features and co-occurrence relationships among medical concepts in EHR data remains a challenge. To address this issue, we propose a new knowledge graph-based approach, which can integrate co-occurrence relationships among medical concepts and numerical information in EHR data. Our approach leverages a concurrency-aware graph learning method and a feature-wise fusion block to enhance knowledge representations and fully exploit the information in patients' EHR data. Experiments using actual EHR data showcases a superior efficacy of our model in forecasting menopausal diseases compared to existing approaches.
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关键词
knowledge graph,EHR,diagnosis,attention
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