Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Abstract

Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision makingneither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.

Computational Health Informatics in the Big Data Age

“…Extracting information from textual documents is a useful information retrieval technique widely used in healthcare informatics [Holzinger et al 2014b;Jung et al 2014;Vijayakrishnan et al 2014]. Using text mining, it is possible to extract information from patient records, reports, lab results, and, generally, clinical notes.…”

Integrating Open Data on Cancer in Support to Tumor Growth Analysis

“…Several open access literature resources exist to apply text mining for finding suitable disease data. However, text mining in biomedical literature is more sophisticated than for clinical data [28]. Only a few databases provide information on cancer incidences and statistics.…”

Machine Learning for Health Informatics

“…Instead of the Euclidean metric the use of a similarity (proximity) measure is sometimes more convenient; the cosine similarity measure is a typical example: the cosine of the angle between two vectors (points in the cloud) reflects how "similar" the underlying weighted combinations of keywords are. Amongst the many different text mining methods (for a recent overview refer to [79]); topological approaches are promising, but need a lot of further research. One of the main tasks of applied topology is to find and analyse higher dimensional topological structures in lower dimensional spaces (e.g.…”