CNN based Multi-Instance Multi-Task Learning for Syndrome Differentiation of Diabetic Patients

TL;DR

This paper introduces a CNN-based multi-instance multi-task learning approach for syndrome differentiation in diabetic patients, treating patient records as images to improve accuracy and robustness in TCM diagnosis.

Contribution

It proposes a novel MIMT-CNN model that maps symptoms to syndromes using object detection inspired techniques, handling missing data and noise effectively.

Findings

Outperforms baseline methods on diabetes dataset

Demonstrates stability with small sample sizes

Effective in noisy and incomplete data conditions

Abstract

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.