  One way to extract patterns from clinical records is to consider each patient
record as a bag with various number of instances in the form of symptoms.
Medical diagnosis is to discover informative ones first and then map them to
one or more diseases. In many cases, patients are represented as vectors in
some feature space and a classifier is applied after to generate diagnosis
results. However, in many real-world cases, data is often of low-quality due to
a variety of reasons, such as data consistency, integrity, completeness,
accuracy, etc. In this paper, we propose a novel approach, attention based
multi-instance neural network (AMI-Net), to make the single disease
classification only based on the existing and valid information in the
real-world outpatient records. In the context of a patient, it takes a bag of
instances as input and output the bag label directly in end-to-end way.
Embedding layer is adopted at the beginning, mapping instances into an
embedding space which represents the individual patient condition. The
correlations among instances and their importance for the final classification
are captured by multi-head attention transformer, instance-level multi-instance
pooling and bag-level multi-instance pooling. The proposed approach was test on
two non-standardized and highly imbalanced datasets, one in the Traditional
Chinese Medicine (TCM) domain and the other in the Western Medicine (WM)
domain. Our preliminary results show that the proposed approach outperforms all
baselines results by a significant margin.
