Automated Detection of Adverse Drug Events from Older Patients’ Electronic Medical Records Using Text Mining

The Swiss Monitoring of Adverse Drug Events (SwissMADE) project is part of the SNSF-funded Smarter Health Care initiative, which aims at improving health services for the public. Its goal is to use text mining on electronic patient reports to automatically detect adverse drug events automatically in hospitalised elderly patients who received anti-thrombotic drugs. The project is the first of its kind in Switzerland: the data is provided by four hospitals from both the German- and French-speaking part of Switzerland, all of which had not previously released electronic patient records for research, making extraction and anonymisation of records one of the major challenges of the project.In this paper, we describe the part of the project concerned with the de-identification and annotation of German data obtained from one of the hospitals in the form of patient reports.All of these reports are automatically de-identified using a dictionary-based approach augmented with manually created rules, and then automatically annotated. For this, we employ our entity recognition pipeline called OGER (OntoGene Entity Recognizer), also a dictionary-based approach, augmented by an adapted transformer model to obtain state of the art performance, to detect drug, disease and symptom mentions in these reports. Furthermore, a subset of reports are manually annotated for drugs and diagnoses by a medical expert, serving as a validation set for the automatic annotations.