Rectified-Linear-Unit-Based Deep Learning for Biomedical Multi-label Data

Disease diagnosis is one of the major data mining questions by the clinicians. The current diagnosis models usually have a strong assumption that one patient has only one disease, i.e. a single-label data mining problem. But the patients, especially when at the late stages, may have more than one disease and require a multi-label diagnosis. The multi-label data mining is much more difficult than a single-label one, and very few algorithms have been developed for this situation. Deep learning is a data mining algorithm with highly dense inner structure and has achieved many successful applications in the other areas. We propose a hypothesis that rectified-linear-unit-based deep learning algorithm may also be good at the clinical questions, by revising the last layer as a multi-label output. The proof-of-concept experimental data support the hypothesis, and the community may be interested in trying more applications.