scholarly journals PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining

2004 ◽  
pp. 216-225 ◽  
Author(s):  
Jae-Hong Eom ◽  
Byoung-Tak Zhang
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Mining System ◽  
Information Mining ◽  
Text Mining System

scholarly journals Construction of biological networks for Korean medical herb using the text-mining system

2015 ◽  
Vol 4 (1) ◽  
pp. 140
Author(s):  
Seok Jong Yu ◽  
Chul Kim ◽  
Yongseong Cho ◽  
Junehawk Lee ◽  
Hyojin Kang
Keyword(s):  
Text Mining ◽  
Biological Networks ◽  
Mining System ◽  
Text Mining System

scholarly journals CLUO: Web-Scale Text Mining System for Open Source Intelligence Purposes

2013 ◽  
Vol 14 (1) ◽  
pp. 45 ◽  
Author(s):  
Przemyslaw Maciolek ◽  
Grzegorz Dobrowolski
Keyword(s):  
Text Mining ◽  
Open Source ◽  
Mining System ◽  
Open Source Intelligence ◽  
Text Mining System

Inductive learning of a knowledge dictionary for a text mining system

2001 ◽  
pp. 247-252 ◽  
Author(s):  
Shigeaki Sakurai ◽  
Yumi Ichimura ◽  
Akihiro Suyama ◽  
Ryohei Orihara
Keyword(s):  
Text Mining ◽  
Inductive Learning ◽  
Mining System ◽  
Text Mining System

scholarly journals Text mining brain imaging reports

2019 ◽  
Vol 10 (S1) ◽  
Author(s):  
Beatrice Alex ◽  
Claire Grover ◽  
Richard Tobin ◽  
Cathie Sudlow ◽  
Grant Mair ◽  
...  
Keyword(s):  
Text Mining ◽  
Epidemiological Studies ◽  
Blind Test ◽  
Mining System ◽  
Brain Scans ◽  
Radiology Reports ◽  
Stroke Study ◽  
Structured Information ◽  
Ischaemic Attack ◽  
Text Mining System

Abstract Background With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes). Results The inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator. Conclusion Automated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.

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