# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
###### Abstract  
Dementia affects approximately 50 million people in the world today, the majority suffering from Alzheimer's disease (AD). The availability of long-term patient data is one of the most important prerequisites for a better understanding of diseases. Worldwide, many prospective, longitudinal cohort studies have been initiated to understand AD. However, this approach takes years to enroll and follow up with a substantial number of patients, resulting in a current lack of data. This raises the question of whether clinical routine datasets could be utilized to extend collected registry data. It is, therefore, necessary to assess what kind of information is available in memory clinic routine databases. We did exactly this based on the example of the University Hospital Bonn. Whereas a number of data items are available in machine readable formats, additional valuable information is stored in textual documents. The extraction of information from such documents is only applicable via text mining methods. Therefore, we set up modular, rule-based text mining workflows requiring minimal sets of training data. The system achieves F1-scores over 95% for the most relevant classes, i.e., memory disturbances from medical reports and quantitative scores from semi-structured neuropsychological test protocols. Thus, we created a machine-readable core dataset for over 8000 patient visits over a ten-year period. clinical text mining; data standardization; semantic interoperability 0000-0001-999-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-  
Dementia (EPAD) cohort [3]. In Germany, the German Center for Neurodegenerative Diseases (DZNE) has started large cohort studies, such as the DZNE Longitudinal Cognitive Impairment and Dementia Study (DELCODE) [4] and the DZNE Clinical Registry Study of Neurodegenerative Diseases (DESCRIBE) [5]. Despite all these efforts, it will take years to collect large subject groups and longitudinal data. Moreover, these cohorts follow their own, often unharmonized, study designs--hence, they might be biased toward their particular study designs. In contrast, hospitals' clinical routine facilities are constantly visited by a broad range of patients. In memory clinics, cognitive deficits are routinely examined and documented together with the resulting diagnosis in medical reports. The most relevant cognitive deficits are memory, language, attention, and planning deficits. Further frequent deficit descriptions focus on general, temporal, and spatial orientation. In structured cohort data, these are core data items together with the estimated onset time. In addition, standardized assessments are highly relevant. In memory clinics, such assessments are often performed on a routine basis as well. One of the most prominent assessments--since it is quickly realizable and broadly covering--is the Mini-Mental State Examination (MMSE). The MMSE represents a short test consisting of just 11 questions that the patient has to answer concerning the cognitive aspects of mental functions [6]. At the University Hospital Bonn, in addition to MMSE, a more in-depth cognitive test, the CERAD (Consortium to Establish a Registry for Alzheimer's Disease; [[https://sites.duke.edu/centerforaging/cerad/](https://sites.duke.edu/centerforaging/cerad/)]([https://sites.duke.edu/centerforaging/cerad/](https://sites.duke.edu/centerforaging/cerad/)), accessed on 25 September 2021) test battery is conducted. In order to quantify memory and language skills, the CERAD test battery offers a broad variety of well-established cognitive tests with a total of 18 test scores and as such, a deeper understanding of cognitive deficits. CERAD is subdivided into seven different subcategories, covering a broad range of commonly observed cognitive deficits. The subcategories comprise the MMSE, verbal fluency, the Boston Naming Test (BNT), construction ability, learning of word lists, recall, and recognition [7; 8].