Danilo Schmidt


2016

pdf bib
A fine-grained corpus annotation schema of German nephrology records
Roland Roller | Hans Uszkoreit | Feiyu Xu | Laura Seiffe | Michael Mikhailov | Oliver Staeck | Klemens Budde | Fabian Halleck | Danilo Schmidt
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

In this work we present a fine-grained annotation schema to detect named entities in German clinical data of chronically ill patients with kidney diseases. The annotation schema is driven by the needs of our clinical partners and the linguistic aspects of German language. In order to generate annotations within a short period, the work also presents a semi-automatic annotation which uses additional sources of knowledge such as UMLS, to pre-annotate concepts in advance. The presented schema will be used to apply novel techniques from natural language processing and machine learning to support doctors treating their patients by improved information access from unstructured German texts.

pdf bib
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.