WeakMeSH: Leveraging provenance information for weakly supervised classification of biomedical articles with emerging MeSH descriptors.

Medical Subject Headings (MeSH) is a hierarchically structured thesaurus created by the National Library of Medicine of USA. Each year the vocabulary gets revised, bringing forth different types of changes. Those of particular interest are the ones that introduce new descriptors in the vocabulary either brand new or those who come up as a product of a complex change. These new descriptors often lack ground truth articles and rendering learning models that require supervision not applicable. Furthermore, this problem is characterized by its multi label nature and the fine-grained character of the descriptors that play the role of classes, requiring expert supervision and a lot of human resources. In this work, we alleviate these issues through retrieving insights from provenance information about those descriptors present in MeSH to create a weakly labeled train set for them. At the same time, we make use of a similarity mechanism to further filter the weak labels obtained through the descriptor information mentioned earlier. Our method, called WeakMeSH, was applied on a large-scale subset of the BioASQ 2018 data set consisting of 900 thousand biomedical articles. The performance of our method was evaluated on BioASQ 2020 against several other approaches that had given competitive results in similar problems in the past, or apply alternative transformations against the proposed one, as well as some variants that showcase the importance of each different component of our proposed approach. Finally, an analysis was performed on the different MeSH descriptors each year to assess the applicability of our method on the thesaurus.

Self-Trained LMT for Semisupervised Learning.

The most important asset of semisupervised classification methods is the use of available unlabeled data combined with a clearly smaller set of labeled examples, so as to increase the classification accuracy compared with the default procedure of supervised methods, which on the other hand use only the labeled data during the training phase. Both the absence of automated mechanisms that produce labeled data and the high cost of needed human effort for completing the procedure of labelization in several scientific domains rise the need for semisupervised methods which counterbalance this phenomenon. In this work, a self-trained Logistic Model Trees (LMT) algorithm is presented, which combines the characteristics of Logistic Trees under the scenario of poor available labeled data. We performed an in depth comparison with other well-known semisupervised classification methods on standard benchmark datasets and we finally reached to the point that the presented technique had better accuracy in most cases.