  In medical real-world study (RWS), how to fully utilize the fragmentary and
scarce information in model training to generate the solid diagnosis results is
a challenging task. In this work, we introduce a novel multi-instance neural
network, AMI-Net+, to train and predict from the incomplete and extremely
imbalanced data. It is more effective than the state-of-art method, AMI-Net.
First, we also implement embedding, multi-head attention and gated
attention-based multi-instance pooling to capture the relations of symptoms
themselves and with the given disease. Besides, we propose var-ious
improvements to AMI-Net, that the cross-entropy loss is replaced by focal loss
and we propose a novel self-adaptive multi-instance pooling method on
instance-level to obtain the bag representation. We validate the performance of
AMI-Net+ on two real-world datasets, from two different medical domains.
Results show that our approach outperforms other base-line models by a
considerable margin.
