Deep Learning-based detection of psychiatric attributes from German mental health records. (May 2022) - Madan, Sumit; Julius Zimmer, Fabian; Balabin, Helena; Schaaf, Sebastian; Fröhlich, Holger; Fluck, Juliane; Neuner, Irene; Mathiak, Klaus; Hofmann-Apitius, Martin; Sarkheil, Pegah
Highlights: A deep learning-based system for extraction of psychiatric attributes from German mental health records. Fine-tuning and application of the GermanBERT model in the clinical domain. Extraction system link the psychiatric attributes to the AMDP symptoms terminology to semantically enhance the data and make it interoperable. Abstract: Background: Health care records provide large amounts of data with real-world and longitudinal aspects, which is advantageous for predictive analyses and improvements in personalized medicine. Text-based records are a main source of information in mental health. Therefore, application of text mining to the electronic health records – especially mental state examination – is a key approach for detection of psychiatric disease phenotypes that relate to treatment outcomes. Methods: We focused on the mental state examination (MSE) in the patients' discharge summaries as the key part of the psychiatric records. We prepared a sample of 150 text documents that we manually annotated for psychiatric attributes and symptoms. These documents were further divided into training and test sets. We designed and implemented a system to detect the psychiatric attributes automatically and linked the pathologically assessed attributes to AMDP terminology. This workflow uses a pre-trained neural network model, which is fine-tuned on the training set, and validated on the independent test set. Furthermore, a traditional NLP and rule-based component linked the recognized mentions to AMDP terminology. In a further step, we applied the system on a larger clinical dataset of 510 patients to extract their symptoms. Results: The system identified the psychiatric attributes as well as their assessment (normal and pathological) and linked these entities to the AMDP terminology with an F1 -score of 86% and 91% on an independent test set, respectively. Conclusion: The development of the current text mining system and the results highlight the feasibility of text mining methods applied to MSE in electronic mental health care reports. Our findings pave the way for the secondary use of routine data in the field of mental health, facilitating further clinical data analyses.