Gregson, Cassie

Hernandez, Robert

Milosevic, Nikola

Nenadic, Goran

English

Current biomedical text mining efforts are mostly focused on extracting information from the body of research articles. However, tables contain important information such as key characteristics of clinical trials. Here, we examine the feasibility of information extraction from tables. We focus on extracting data about clinical trial participants. We propose a rule-based method that decomposes tables into cell level structures and then extracts information from these structures. Our method performed with a F-measure of 83.3% for extraction of number of patients, 83.7% for extraction of patient’s body mass index and 57.75% for patient’s weight. These results are promising and show that information extraction from tables in biomedical literature is feasible

The University of Manchester - Institutional Repository

Extracting patient data from tables in clinical literature: Case study on extraction of BMI, weight and number of patients

Nikola Milosevic

Cassie Gregson

Robert Hernandez

Goran Nenadic

Crossref

Extracting Patient Data from Tables in Clinical Literature - Case Study on Extraction of BMI, Weight and Number of Patients

https://pure.manchester.ac.uk/ws/files/34231319/FULL-TEXT.PDF

Extracting patient data from tables in clinical literature: Case study on extraction of BMI, weight and number of patients

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The University of Manchester - Institutional Repository

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