# Information Extraction from German Clinical Care Documents in Context of Alzheimer's Disease
## 2 Related Work  
In the past, the scientific community organized and published a series of shared tasks and datasets to promote exchange in the clinical NLP domain. A prominent example is the \"Informatics for Integrating Biology and the Beside\" (i2b2) initiative ([[https://www.i2b2.org/NLP/DataSets/Main.php](https://www.i2b2.org/NLP/DataSets/Main.php)]([https://www.i2b2.org/NLP/DataSets/Main.php](https://www.i2b2.org/NLP/DataSets/Main.php)), accessed on 25 September 2021), now known as the \"National NLP Clinical Challenges\" (n2c2 ; [[https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/](https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/)]([https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/](https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/)), accessed on 25 September 2021) that aims to build a framework that provides clinical data to researchers. \"SemEval Clinical TempEval challenges\" and \"TREC Precision Medicine/Clinical Decision Support Track\" are further well-known communities that regularly campaign new tasks and datasets for clinical NLP. In general, these challenges focus on clinical NER tasks, such as the detection of diseases, symptoms, drugs/medication, or body parts [11] and on the temporal ordering of clinical events. Clinical text mining systems are based either on rule-based approaches or machine learning (ML) techniques. In both cases, raw text is often preprocessed first by performing sentence detection, tokenization, stemming, part-of-speech tagging, or stopword removal. For the rule-based approach, a subsequent step involves the development of manually created rules that typically use regular expressions. With the availability of larger training datasets, the creation of ML-based applications using methods such as support vector machines (SVMs), conditional random fields (CRFs), or neural networks (NNs), have become popular. For instance, the 2010 i2b2 challenge introduced a task on medical concept detection (such as symptoms and procedures), where most systems were based on CRFs. The performance of the best systems in terms of F1-score ranges from 0.788 to 0.852 [12]. [PERSON] et al. developed a rule and dictionary-based system to extract medication findings (such as drug name, strength and frequency) from unstructured clinical narratives [13]. The authors achieved an F1-score of 93.2% for drug names. [PERSON] et al. [14] demonstrated two clinical applications focusing on the mining of unstructured German physician reports with rule-based approaches. The first application dealt with information extraction from surgical reports of endoprosthesis implantations, e.g., anatomic region (knee/hip), reasons and type of surgery as well as surgical complications. The second application identified and interpreted TNM classifications of malignant tumors from pathology reports (where T, N, and M stand for tumor, lymph node, and metastasis, respectively). More recently, [PERSON] et al. developed a pipeline to automatically annotate German clinical colorectal cancer notes. Using a dictionary-based approach, including pre-processing steps, they annotated UMLS concepts, achieving F1-scores in the range of 64.52% to 98.74% for the different classes [15]. As mentioned, negation detection plays an important role in clinical text mining. The well-known negation detector written for English medical reports is _NegEx_, which is a regular expression-based algorithm that works at the sentence level [10]. An extension of the NegEx algorithm, called _ConText_, focuses additionally on the recognition of hypothetical or historical statements as well as whether the clinical conditions are experienced by someone other than the patient [16]. [PERSON] et al. [17] adapted NegEx to German clinical reports by the creation of a new list of trigger terms, which is also publicly available. Recent studies have shown the potential of using electronic health records as a source of valuable information for Alzheimer's disease research. [PERSON] et al.