Deep Learning to Classify Radiology Free-Text Reports

Intelligent Word Embeddings of Free-Text Radiology Reports

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the ambiguity and subtlety of natural language. We propose a hybrid strategy that combines semanticdictionary mapping and word2vec modeling for creat...

Automatically structuring free-text radiology reports using a machine learning algorithm

Machine Learning to Automate the Assignment of Diagnosis Codes to Free-text Radiology Reports: a Method Description

We introduce a multi-label classification system for the automated assignment of diagnostic codes to radiology reports. The system is a cascade of text enrichment, feature selection and two classifiers. It was evaluated in the Computational Medicine Center’s 2007 Medical Natural Language Processing Challenge and achieved a 87.7% micro-averaged F1-score and third place out of 44 submissions in t...

Automatic Classification of Free-Text Radiology Reports to Identify Limb Fractures using Machine Learning and the SNOMED CT Ontology

OBJECTIVE To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. MATERIALS AND METHODS 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician ...

A text processing pipeline to extract recommendations from radiology reports

Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology recommendations is an important barrier to ensuring timely follow-up of patients especially with non-acute incidental findings on imaging examinations. In this paper, we present a text processing pipeline to automat...