# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
## 3 Materials and Methods
### Medical Report Pipeline
#### 3.2.3 Structure of the Medical Report Pipeline  
An overview of the structure of the medical report pipeline can be seen in Figure 1 (on top). The pipeline consists of several steps that are performed in a consecutive manner. First, the textual contents from MS Word documents in DOC and DOCX format and text-based fields of the _Agfa ORBIS_ hospital information system (HIS) were parsed to prepare the medical report documents for further analysis (e.g., cleaning, harmonizing encoding, converting to plain-text format). Furthermore, basic preprocessing steps, i.e., sentence detection, tokenization, and lemmatization are applied. Second, we apply a document decomposer to structure the text documents into different paragraphs. This is required because our information extraction components are highly modularized and are, therefore, specifically designed for different sections appearing in the documents. In this way, each of the IE components can easily be embedded in new workflows for different clinics. These IE components extract various annotations for the defined classes. Finally, we apply a post-processing step in order to detect negated statements. In the following, the various parts of the pipeline are described in detail. _Document decomposition:_ This module subdivides the medical reports into nine different sections, typical of medical reports created in neurology departments (e.g., anamness, and epicrisis). For their recognition, we created a terminology with the usual titles of the corresponding paragraphs. These titles were automatically extracted from the HIS and were further extended by a medical expert. The titles were embedded in a dictionary, which is used in a rule-based system to detect the begin and end of sections. Example rules with detailed explanations can be found in the Appendix A (see Table A4). _Cognitive Deficits Detection:_ To recognize AD-specific cognitive deficits, we developed four separate components that run on their associated sections (see Table 3) extracted from the medical reports. By analyzing the annotated training data, typical expressions for each of the entity classes could be identified. The general working mechanism of these components is similar to the document decomposer: we combine dedicated terminologies with regular expressions that use lemmas for detection. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Class** & **Level** & **Precision** & **Recall** & **F1-Score** \\ \hline Memory deficit & Sentence & 0.9216 & 0.8103 & 0.8624 \\  & Document & 0.95 & 1.0 & 0.9744 \\ Language deficit & Sentence & 0.8667 & 0.619 & 0.7222 \\  & Document & 0.8333 & 1.0 & 0.9091 \\ Attention deficit & Sentence & 0.8333 & 0.7143 & 0.7692 \\  & Document & 0.75 & 0.75 & 0.75 \\ Planning deficit & Sentence & 0.5714 & 0.3636 & 0.4444 \\  & Document & 0.6667 & 0.5714 & 0.6154 \\ Orientation deficit & Sentence & 0.75 & 0.6 & 0.6667 \\  & Document & 1.0 & 0.8571 & 0.9231 \\ Onset time & Sentence & 0.9231 & 0.96 & 0.9412 \\  & Document & 0.9231 & 0.96 & 0.9412 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inter-annotator agreement on both sentence and document level. Moreover, we normalize the detected entities to map them to the corresponding variables from the DZNE cohort study. The amount of rules needed for a single entity class varies strongly. The detection of the memory deficits is the most complex. Here, we first detect simple phrasings, such as \"vergessen\" or \"haufig verlegen\" (engl. \"to forget\", \"to mislay often\"). Second, we detect words and corresponding negation terms that indicate a memory deficit only if they occur together, e.g., \"to not remember\". An example can be found in the Appendix A. Third, we unmark detected annotations that contain the term \"Langzeit\" (engl. \"long term\"). We relate all the cognitive deficits to a detected onset time. Therefore, sophisticated rules were developed in order to detect the relative and the absolute onset (two years ago vs. since January 2019).