Changes between Version 15 and Version 16 of i2b2 AUG 2013

Timestamp:

06/24/13 12:30:57 (11 years ago)

Author:

Richard Bramley

Comment:

--

i2b2 AUG 2013

@@ -147 +147 @@
 
 === [=#NLP6 Active Learning for Ontology-based Phenotyping] ===
+
+Presentation on a proposed Active Learning method to reduce the number of training examples an algorithm requires for machine learning, as annotation of examples is slow and expensive.
+
+The normal method for training an NLP algorithm is to randomly select a potion of the data to annotate.  This method proposes initially annotating a small number of random samples and selecting subsequent samples to annotate based on the algorithm's output for that sample having a low ''prediction margin''.
+
+'''Prediction Margin = confidence for class A - confidence for class B'''
+
+In other words, how sure it is that the best answer is correct.
+
+The presentation showed that in general (but not for every example) the Active Learning method needed fewer annotated examples to reach a high level of confidence.
+
 === [=#NLP7 Conclusion] ===
+
+These are the conclusions that I (Richard Bramley) drew from the NLP conference.
+
+1. There are a lot of tools and resources available.  The integration of the cTakes tools with UMLS seems especially useful.
+1. Access to clinician time to train NLP algorithms is '''essential'''.
+1. The statistical analysis of the results is beyond my current capabilities.  I may need some training in this area.
 
 == Academic User Group ==