# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
###### Abstract
Because the full CERAD test battery is time consuming and quite exhausting for the patient, it is not measured on every visit, but mostly during the first, providing a detailed assessment right at the start. In follow-up visits, often the MMSE score is measured only (instead of the whole CERAD test battery), estimating any cognitive decline. For each patient, the above-mentioned data points are captured in memory clinics on a routine basis in various documents. However, these clinical documents are not structured, as they consist of human-written texts. In this study, we therefore develop an information extraction workflow in order to automatically extract the relevant information from various clinical document types originated in the neurology department. We make use of text mining methods based on natural language processing (NLP). The application of text mining methods in hospital context is also known as clinical text mining and poses several additional challenges in comparison to general text mining. Medical reports appear to not have a common and explicitly defined structure (as, for example, compared to a scientific paper). However, usual named form fields, paragraph headings, and topics/terms recurrently appear, especially when using a similar hospital information system or analyzing documents from one department. Furthermore, data privacy considerations limit the access to a feasible amount of training and test data in the local language. Publicly available datasets are often inaccessible or non-existing for languages other than English. Moreover, setting up a compliant software environment requires substantial efforts. Often, just small numbers of manually anonymized documents are available. As the clinical records to be processed are written in German, commonly available medical text mining and NLP components for English language cannot be applied. Similarly, existing medical terminologies, such as ICD-10, are mainly focused on accounting processes, not matching the expressions used in medical reports [9]. Other important terminologies do not necessarily exist in the given local language. Furthermore, diagnosis criteria, such as assessment scales for neurodegenerative diseases, may differ between countries and may even not be covered by any terminology yet. A well-known special characteristic of medical reports, especially in the context of anamnesis, is often the negation of mentioned diseases and symptoms for the sake of explicitly excluding them [10]. Therefore, the detection of negations needs to be considered well in clinical text mining. Similarly, family and disease history information need to be separated from current findings. In this study, we implemented a text mining workflow to structure routine data hidden in German clinical care documents of a memory clinic. For this purpose, we created a training and a test dataset, which were both manually annotated by medical experts. The training dataset builds the foundation for the implementation of rule-based pipelines to extract information from medical and neuropsychological test reports of AD patients. In addition, we present a detailed evaluation of the workflow and, furthermore, show the valuable results collected by applying the workflow on a large memory clinic dataset. By structuring the large memory clinic dataset, we expect--despite moving toward digital transformation of the clinic itself--to extend cohort data with clinical routine data to support future research. We also expect to improve the case findings, due to the higher availability of clinical information that were hidden before in clinical care documents.