Viviana Cotik


A Corpus for Outbreak Detection of Diseases Prevalent in Latin America
Antonella Dellanzo | Viviana Cotik | Jose Ochoa-Luna
Proceedings of the 24th Conference on Computational Natural Language Learning

In this paper we present an annotated corpus which can be used for training and testing algorithms to automatically extract information about diseases outbreaks from news and health reports. We also propose initial approaches to extract information from it. The corpus has been constructed with two main tasks in mind. The first one, to extract entities about outbreaks such as disease, host, location among others. The second one, to retrieve relations among entities, for instance, in such geographic location fifteen cases of a given disease were reported. Overall, our goal is to offer resources and tools to perform an automated analysis so as to support early detection of disease outbreaks and therefore diminish their spreading.


Annotation of Entities and Relations in Spanish Radiology Reports
Viviana Cotik | Darío Filippo | Roland Roller | Hans Uszkoreit | Feiyu Xu
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

Radiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods are very popular to address information extraction, but are usually domain and language dependent. To train new classification models, annotated data is required. Moreover, annotated data is also required as an evaluation resource of information extraction algorithms. However, one major drawback of processing clinical data is the low availability of annotated datasets. For this reason we performed a manual annotation of radiology reports written in Spanish. This paper presents the corpus, the annotation schema, the annotation guidelines and further insight of the data.


Syntactic methods for negation detection in radiology reports in Spanish
Viviana Cotik | Vanesa Stricker | Jorge Vivaldi | Horacio Rodriguez
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

Negation Detection in Clinical Reports Written in German
Viviana Cotik | Roland Roller | Feiyu Xu | Hans Uszkoreit | Klemens Budde | Danilo Schmidt
Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)

An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that are negated or just speculated. The system has been applied to two different types of German clinical texts: clinical notes and discharge summaries. Our approach is built on top of NegEx, a well known algorithm for identifying non-factive mentions of medical findings. In this work, we adjust a previous adaptation of NegEx to German and evaluate the system on our data to detect negation and speculation. The results are compared to a baseline algorithm and are analyzed for both types of clinical documents. Our system achieves an F1-Score above 0.9 on both types of reports.