Author: Lu, William; Ng, Raymond
Title: Automated Analysis of Public Health Laboratory Test Results. Cord-id: uvvfjckv Document date: 2020_1_1
ID: uvvfjckv
Snippet: This study investigates the use of machine learning methods for classifying and extracting structured information from laboratory reports stored as semi-structured point-form English text. This is a novel data format that has not been evaluated in conjunction with machine learning classifiers in previous literature. Our classifiers achieve human-level predictive accuracy on the binary Test Performed and 4-class Test Outcome labels. We consider symbolic approaches for predicting the highly multi-
Document: This study investigates the use of machine learning methods for classifying and extracting structured information from laboratory reports stored as semi-structured point-form English text. This is a novel data format that has not been evaluated in conjunction with machine learning classifiers in previous literature. Our classifiers achieve human-level predictive accuracy on the binary Test Performed and 4-class Test Outcome labels. We consider symbolic approaches for predicting the highly multi-class Organism Genus and Organism Species labels. Results are discussed from the viewpoint of interpretability and generalizability to new incoming laboratory reports. Code has been made public at https://github.com/enchainingrealm/UbcDssgBccdc-Research/tree/master/src.
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