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Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a few visits per patient and a few observations per disease can be exploited. Although meta-learning is widely adopted to address the data sparsity problem in general domains, directly applying it to healthcare data is less effective, since it is unclear how to capture both the temporal relations in clinical visits and the complicated relations among syndromic diseases for precise personalizeddoi:10.1145/3477495.3532020 fatcat:g372z4mz4nfuzerufyuoy2safa