Automated Organ-Level Classification of Free-Text Pathology Reports to Support a Radiology Follow-up Tracking Engine.

ABSTRACT:

Purpose

To evaluate the performance of machine learning algorithms on organ-level classification of semistructured pathology reports, to incorporate surgical pathology monitoring into an automated imaging recommendation follow-up engine.

Materials and methods

This retrospective study included 2013 pathology reports from patients who underwent abdominal imaging at a large tertiary care center between 2012 and 2018. The reports were labeled by two annotators as relevant to four abdominal organs: liver, kidneys, pancreas and/or adrenal glands, or none. Automated classification methods were compared: simple string matching, random forests, extreme gradient boosting, support vector machines, and two neural network architectures-convolutional neural networks and long short-term me

SUBMITTER: Steinkamp JM 

PROVIDER: S-EPMC8017395 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature