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Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks

Conference ·
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  1. ORNL
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To trust model predictions, it is important to ensure new data scored by the model comes from the same population used for model training. If the model is used to score new data different than the model’s training data, then predictions and model performance metrics cannot be trusted. Identifying and excluding these anomalous data points is an important task when using models in the real world. Traditional machine learning algorithms and classifiers don’t have the capability to abstain in this case. Here we propose a data-novelty detection algorithm for the Convolutional Neural Network classifier, yielding a rejection score for each new data point scored. It is a post-modeling procedure which examines the distribution of convolution filters to determine if the prediction should be trusted. We apply this algorithm to an information extraction model for a natural language text corpus. We evaluated the algorithm performance using a primary cancer site classification model applied to cancer pathology reports. Results demonstrate that the algorithm is an effective way to exclude cancer pathology reports from model scoring when they do not contain the expected information necessary to accurately classify the primary cancer type.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1509553
Resource Relation:
Conference: INNS Big Data and Deep Learning 2019 - Genoa, , Italy - 4/16/2019 4:00:00 AM-4/18/2019 4:00:00 AM
Country of Publication:
United States
Language:
English

References (9)

Uncertainty Quantification: Theory, Implementation, and Applications book January 2013
On the Exact Variance of Products journal December 1960
The 2007 WHO Classification of Tumours of the Central Nervous System journal July 2007
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research journal January 2008
Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports journal January 2018
Deep learning journal May 2015
Bayesian Data Analysis book November 2013
Filter pruning of Convolutional Neural Networks for text classification: A case study of cancer pathology report comprehension conference March 2018
Convolutional Neural Networks for Sentence Classification conference January 2014

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