Automatic Structuring of Radiology Free-Text Reports

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

Automatic structuring of radiology reports: harbinger of a second information revolution in radiology.

Over the past 2 decades, rapid technologic advances have enabled radiologists to acquire, transmit, process, display, and store multidimensional digital images. Despite the steady advances in the way radiologists manage images, similar technologic innovations have not yet affected how radiologists communicate with their colleagues. This discrepancy is particularly striking because the informati...

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...

Structuring Free-text Microbiology Culture Reports For Secondary Use

Microbiology lab culture reports are a frequently used diagnostic tool for clinical providers. However, their incorporation into clinical surveillance applications and evidence-based medicine can be severely hindered by the free-text nature of these reports. In this work, we (1) created a microbiology culture template to structure free-text microbiology reports, (2) generated an annotated micro...

Automatic Text Decomposition and Structuring

Sophisticated text similarity measurements are used to determine relationships between natural-language texts and text segments. The resulting linked hypertext maps are used to identify different text types and text structures, leading to improved text access and utilization. Examples of text decomposition are given for expository and non-expository texts. The vector processing model of retriev...