  Syndrome differentiation in Traditional Chinese Medicine (TCM) is the process
of understanding and reasoning body condition, which is the essential step and
premise of effective treatments. However, due to its complexity and lack of
standardization, it is challenging to achieve. In this study, we consider each
patient's record as a one-dimensional image and symptoms as pixels, in which
missing and negative values are represented by zero pixels. The objective is to
find relevant symptoms first and then map them to proper syndromes, that is
similar to the object detection problem in computer vision. Inspired from it,
we employ multi-instance multi-task learning combined with the convolutional
neural network (MIMT-CNN) for syndrome differentiation, which takes region
proposals as input and output image labels directly. The neural network
consists of region proposals generation, convolutional layer, fully connected
layer, and max pooling (multi-instance pooling) layer followed by the sigmoid
function in each syndrome prediction task for image representation learning and
final results generation. On the diabetes dataset, it performs better than all
other baseline methods. Moreover, it shows stability and reliability to
generate results, even on the dataset with small sample size, a large number of
missing values and noises.
