Supporting clinical trial data curation and integration

with table miningNikola Milosevic1, Cassie Gregson3, Robert Hernandez3, Goran Nenadic1,2

1School of Computer Science, University of Manchester2 The Farr Institute @HeRC3AstraZeneca

Clinical trial publications• Around 800 000 clinical trials in PubMed• Difficult to digest/search• Text mining approaches• But tables and figures are

often not processed

Tables in publications• Present factual information• Usually:• Experimental settings (i.e. demographics)• Findings and results (e.g. DDI, side effects, adverse events…)• Background information (previous research, datasets, etc.)• Examples

• Important information about trials

Extraction and curation of table data

Challenges• Complex structure• Table dimensionality (1, 2, multi-dimensional)• Visual relationships

• Dense content• Ambiguous short text• Lack of context• Acronyms and abbreviations• Incomplete information

Table analysis overview

Table types (1)• 4 types: list, matrix, super-row and multi-tables• List table:

Table types (2)• Matrix table

Table types (3)• Super-row table

Table types (4)• Multi-table

Example of decomposition

Example of decomposition

Example of decomposition

Results

Next steps• Add semantic annotations• Link patterns in data cells with its meaning• Build/Expand knowledge bases• Relate to existing knowledge on the semantic web

Annotation schema• Meta-data• Paper (name, abstract, authors, publisher)• Authors (names, emails, affiliations)• Table (caption, footers)• Cells (content, role)• Inter-cell relationships• Semantics (links to ontologies, dictionaries, knowledge bases)

Summary• Tables contain valuable information such as settings or

results • System for extraction and curation of table data• Decomposition and annotation of the tables• Accuracy of 85%

• Semantic analysis and information extraction

nikola.milosevic@manchester.ac.uk