title: Text mining for improved exposure assessment.
abstract: Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure information available from web-based databases, such as PubMed, is undoubtedly a great asset to the scientific community. However, manual retrieval of relevant published information is an extremely time consuming task and overviewing the data is nearly impossible. Here, we present the development of an automatic classifier for chemical exposure information. First, nearly 3700 abstracts were manually annotated by an expert in exposure sciences according to a taxonomy exclusively created for exposure information. Natural Language Processing (NLP) techniques were used to extract semantic and syntactic features relevant to chemical exposure text. Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. The resulting classifier demonstrates good performance in the intrinsic evaluation. We also show that the classifier improves information retrieval of chemical exposure data compared to keyword-based PubMed searches. Case studies demonstrate that the classifier can be used to assist researchers by facilitating information retrieval and classification, enabling data gap recognition and overviewing available scientific literature using chemical-specific publication profiles. Finally, we identify challenges to be addressed in future development of the system.
authors.affiliation: Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
authors.forename: Kristin
authors.identifier: http://orcid.org/0000-0003-0398-0289
authors.initials: K
authors.lastname: Larsson
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Simon
authors.initials: S
authors.lastname: Baker
authors.affiliation: Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
authors.forename: Ilona
authors.initials: I
authors.lastname: Silins
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Yufan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
authors.forename: Ulla
authors.initials: U
authors.lastname: Stenius
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.|Language Technology Lab, DTAL, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Anna
authors.initials: A
authors.lastname: Korhonen
authors.affiliation: Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.
authors.forename: Marika
authors.initials: M
authors.lastname: Berglund
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D004784:Environmental Monitoring / Q000379:methods*; D004787:Environmental Pollution; D006801:Humans; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D039781:PubMed; D012660:Semantics