# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
## 5 Discussion
As the written free text is based on individual patient narratives, both the content and wording can differ enormously. The determination of IAA represents a good indicator for the expected extraction quality. In our evaluation, the automatic system achieves quite comparable results to IAA; in some cases, the system even beats the IAA scores. Overall, the recognition of various sections achieves an F1-score of 97%. For four of the detected sections, we developed further dedicated rules to extract diagnosis terms, the date of visit, the MMSE score with date and the cognitive deficits. Whereas we reached F1-scores above 90% for the first three entity classes, both the manual and automatic recognition of the six different cognitive deficits experience a wide range of F1-scores. The most often occurring deficit--the memory deficit--can be extracted reliably with an F1-score of 95% on document level. One of the important steps is also the transfer of the extracted data back into the hospital information system. This is needed to ensure proper access to the extracted data, thereby enabling its re-use. In the present study, we used a specific data model tailored especially for the University Hospital Bonn to achieve the goal of extending cohort data with their clinical routine data. In future, it will be preferable to adapt to a more generic data model that can be applied across different hospitals and institutions. Such a schema could be modeled with Fast Healthcare Interoperability Resources (FHIR; [[https://hl7.org/FHIR](https://hl7.org/FHIR)]([https://hl7.org/FHIR](https://hl7.org/FHIR)), accessed on 25 September 2021), a standard for data exchange in health care. In future, we aim to apply the developed pipelines in different hospitals and analyze how far the workflow needs to be adapted. In Germany, the CERAD test battery is commonly used to assess patients for AD. Even if different hospitals store these results differently, chances are quite high that our pipeline can be (at least partly) applied. The diversity of the clinical information systems and document formats in clinics can only be coped by enhancing standardization, which is yet hard to achieve. Therefore, we chose the best current option, i.e., to modularize the text mining pipeline, to allow for easy customization of various components to cope with the available diversity. With the application of our developed workflow within the University Hospital, we could gain valuable, machine-readable data that can be used to enlarge cohort study data, such as the clinical study DESCRIBE [5] conducted currently in Germany. To achieve this semantic interoperability, we mapped our extracted attributes to the variables used by DESCRIBE ([[https://medical-data-models.org/details/13956](https://medical-data-models.org/details/13956)]([https://medical-data-models.org/details/13956](https://medical-data-models.org/details/13956)), accessed on 25 September 2021) that can be found at Medical Data Models Portal [21]. Thus, the number of patient data can be increased about tenfold, which will allow for more significant analysis. Moreover, using the patient data from clinics could allow a realistic sample of the population, and a possible cohort selection bias could be significantly reduced. In the long run, the integration of such structured routine data with the cohort studies data and their combined analysis will enable new routes for Alzheimer's disease research.