DTL Semantic Analysis of radiology Reports utilizing Lexicon

Unlocking large volumes of knowledge locked in natural text

We are looking at large amounts (tens of thousands) of clinical case transcriptions. A few hundred of these cases have been classified with observations from the radiologists, for example: unusual mass of tissue on the upper lobe of the left lung. However, annotating all of these cases by hand is extremely time consuming and error prone.

We will use NLP & ML tools to learn from the text and automatically annotate the bulk of the clinical cases. Similar projects have been run in the past, but the annotations produced were not of sufficient quality.

The resulting NLP tool could give a tremendous boost to clinical radiology, unlocking large volumes of knowledge currently locked in natural text.

Participating organisations

Dutch Techcenter for Life Sciences
Netherlands eScience Center
Life Sciences
Life Sciences
Philips
Tata Memorial Centre
Social Sciences & Humanities
Social Sciences & Humanities
Maastricht University Medical Center+

Team

Jisk Attema
eScience Coordinator
Netherlands eScience Center
Carlos Martinez-Ortiz
Carlos Martinez-Ortiz
Community Manager
Netherlands eScience Center
LW
Leonard Wee
Principal investigator
Maastricht Universitair Medisch Centrum

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