Multilabel Text Classification in Biomedical Domain

Multilabel text classification is a complex task in the field of natural language processing (NLP), involving the assignment of multiple labels to a single piece of text. Its applications in biomedical domain includes various tasks like disease classification, discharge summary classification, biomarkers prediction, and many more. Nonetheless, this approach presents significant challenges due to the nuanced nature of conveying multiple facets within a single textual input. Accurate evaluation measures of such models necessitate specialized metrics like precision, recall, F1-score, alongside advanced measurements like Hamming loss, one error, and ranking loss. The chapter highlights these parameters along with its description. In the realm of multilabel text classification, support vector machine (SVM) offers a valuable solution by effectively handling high-dimensional text features. We propose SVM-based model for multilabel text classification on biomedical domain. Our proposed model involves integration of variety of features, which harnesses statistical, linguistic, and biomedical aspect of the PubMed abstracts. The envisioned model harmoniously integrates linguistic, statistical, and biomedical features to extract a holistic representation of textual data. Linguistic components capture syntax and semantics, statistical elements unravel word distributions, and biomedical attributes facilitate recognition of domain-specific context such as highlighting the role of proteins/RNA/DNA/ cell structures or chemicals.