Introduction This work describes the Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 challenge for extracting medication, indication, and adverse drug events (ADEs) from electronic health record (EHR) notes. Objective The goal of MADE is to provide a set of common evaluation tasks to assess the state of the art for natural language processing (NLP) systems applied to EHRs supporting drug safety surveillance and pharmacovigilance. We also provide benchmarks on the MADE dataset using the system submissions received in the MADE 2018 challenge. Methods The MADE 1.0 challenge has released an expert-annotated cohort of medication and ADE information comprising 1089 fully de-identified longitudinal EHR notes from 21 randomly selected patients with cancer at the University of Massachusetts Memorial Hospital. Using this cohort as a benchmark, the MADE 1.0 challenge designed three shared NLP tasks. The named entity recognition (NER) task identifies medications and their attributes (dosage, route, duration, and frequency), indications, ADEs, and severity. The relation identification (RI) task identifies relations between the named entities: medication-indication, medication-ADE, and attribute relations. The third shared task (NER-RI) evaluates NLP models that perform the NER and RI tasks jointly. In total, 11 teams from four countries participated in at least one of the three shared tasks, and 41 system submissions were received in total. Results The best systems F 1 scores for NER, RI, and NER-RI were 0.82, 0.86, and 0.61, respectively. Ensemble classifiers using the team submissions improved the performance further, with an F 1 score of 0.85, 0.87, and 0.66 for the three tasks, respectively. Conclusion MADE results show that recent progress in NLP has led to remarkable improvements in NER and RI tasks for the clinical domain. However, some room for improvement remains, particularly in the NER-RI task. Part of a theme issue on "NLP Challenge for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0)" guest edited by Feifan Liu, Abhyuday Jagannatha and Hong Yu.  The MADE (Medication and Adverse Drug Events from Electronic Health Records) 1.0 corpus comprises 1089 electronic health records with detailed named entity and relation annotations. We provide benchmark results from and analysis of the MADE 1.0 corpus using system submissions in the MADE 1.0 challenge. MADE 1.0 results suggest that machine learning systems can be useful for automated extraction of adverse drug events and related entities from unstructured texts but that room for improvement remains.
More recently, the Medication and Adverse Drug Events from Electronic Health Records (MADE) shared-task has been organised to detect medications and adverse drug events in electronic health records (EHRs) REF .
Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0)
Drug Safety
Drug Safety
Medicine