Chapter 16: Text Mining for Translational Bioinformatics

Abstract

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research—translating basic science results into new interventions—and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research

“…With structured billing and claims data complemented by the rich content of clinical text, researchers argue that much of clinical medicine can benefit from analyzing data already in clinical data warehouses [6,7,10-17]. Investigators can use this data to reveal associations and predictors for hard to detect, yet severe, disease complications and co-morbidities.…”

Barriers to Open Access Publishing: Views from the Library Literature

“…Some journals contained very structured abstracts while others only provided a single sentence or did not state the purpose and/or conclusion of the study. Other investigations have also shown that when using text mining methods, abstracts have different structural and content characteristics from article bodies even when the abstracts are similarly structured [49,50].…”

SEACOIN 2.0 – an interactive mining and visualization tool for information retrieval, summarization, and knowledge discovery

“…Biomedical text mining is a growing research area that involves information retrieval, information extraction, named entity recognition, knowledge discovery, and summarization from scientific literature (Rebholz-Schuhmann 2012, Cohen 2013). The concept of literature based discovery was established by Swanson based on his findings of the implicit relationship between fish-oil and Raynaud's disease (Swanson 1986).…”