An Approach to Detect Negation on Medical Documents in Spanish

The adoption of hospital EHR technology is significantly growing and expected to grow. Digitalized information is the basis for health analytics. In particular, patient medical records contain valuable clinical information written in narrative form that can only be extracted after it has been previously preprocessed with Natural Language Processing techniques. An important challenge in clinical narrative text is that concepts commonly appear negated. Though worldwide there are nearly 500 million Spanish speakers, there seems to be no algorithm for negation detection in medical texts written in that language.

Thus this paper presents an approach to adapt the NegEx algorithm to be applied to detect negation regarding clinical conditions in Spanish written medical documents. Our algorithm has been trained with 500 texts where 422 different sentences and 267 unique clinical conditions were identified. It has been tested for negated terms showing an accuracy obtained is of 83,37%. As in the detection of definite affirmed conditions, the results show an accuracy of 84,78%.