# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
## 5 Discussion  
In this study, we have presented and evaluated a text mining workflow that extracts AD-specific patient attributes from clinical routine data of the neurology department with high extraction performance. Designing and implementing the workflow that can cope with a heterogeneous clinical data warehouse required identifying and solving many hurdles: first, we needed to identify what kind of information is stored in which resource; second, we found the database fields that include the relevant semi-structured data; third, for the textual documents (such as MS Word), we had to assess if the information can be found and extracted reliably; and fourth, we had to determine how the training and test datasets could be generated while at the same time complying with data privacy regulations. Especially for the last issue, it became clear that it will take a significant time investment of medical experts to annotate and anonymize a certain amount of data. Due to the low number of training data and our preliminary experiments, we decided to focus on rule-based implementation, even though the current trends (such as using neural networks) show high potential for entity recognition tasks [24] that are also applied in the clinical domain [11]. The success of the state-of-the-art methods depends on the pre-training task, which is performed on huge amounts of both nonspecific and domain-specific data. The _Clinical-BERT_[25] model, for instance, is pre-trained on approximately two million clinical notes from the publicly available MIMIC-III corpus [26]. As there is no corresponding dataset available for the German language, a traditional rule-based approach was considered the best choice. It is expected that this situation will change in the future, as the need for appropriate training data is rising also in the German clinical domain. As more training data become available, we will extend our workflow with machine learning models to predict certain classes, which will certainly decrease the manual effort of adapting rules. The developed text mining workflow consists of two pipelines that process two different types of documents. Whereas the neuropsychological test reports are already in a semi-structured (tabular) format, the medical reports contain free text. For the NPT reports, the extraction of the 18 cognitive test scores reaches an F1-score of 96%. This result shows that in some cases, it is easily--and with low technical effort--possible to make high-value information machine readable. We should not neglect these \"low hanging fruits\", but focus on making these data available for further research. The medical report pipeline is more complex and consists of several components. The first step is the document decomposition that represents the basis of the extraction of different entity classes in a consecutive step. Therefore, we developed dedicated modules for these sections. The detection of the cognitive deficits in the _anamnesis_ paragraph is quite sophisticated and needed careful considerations while defining the extraction rules. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. of Visits** & **No. of Patients** & **No. of Cognitive Deficits** & **No. of Test** & **No. of MMSE** & **No. of Diagnosis** \\  & & & **Batteries** & **Scores** & **Terms** \\ \hline First visit & 4834 (1042) & 2347 & 699 & 1971 & 1664 \\ Second visit & 3792 (965) & 1134 & 906 & 849 & 828 \\ Third visit & 2827 (775) & 898 & 353 & 532 & 676 \\ Fourth visit & 2052 (495) & 648 & 166 & 354 & 497 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of patient data collected from the memory clinic. It covers different classes and patient visits. For the patients, the first number indicates the number of patients having at least the indicated amount of visits, whereas the number in brackets indicates the number of patients with exactly the mentioned amount of visits. For example, 2052 patients have at least 4 visits and 495 from them have exactly 4 visits.