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.
REF proposed a rule based solution for extracting table data from tables in clinical documents in which the data is firstly decomposed into cell level structures depending on their complexity and then information is extracted from these cell structures.
Extracting Patient Data from Tables in Clinical Literature - Case Study on Extraction of BMI, Weight and Number of Patients
HEALTHINF
Computer Science