Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models

An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care. Healthcare providers or the patients themselves may initiate changes to patient medication. Medication changes take many forms, including prescribed medication and associated dosage modification. These changes provide information about the overall health of the patient and the rationale that led to the current care. Future care can then build on the resulting state of the patient. This work explores the automatic extraction of medication change information from free-text clinical notes. The Contextual Medication Event Dataset (CMED) is a corpus of clinical notes with annotations that characterize medication changes through multiple change-related attributes, including the type of change (start, stop, increase, etc.), initiator of the change, temporality, change likelihood, and negation. Using CMED, we identify medication mentions in clinical text and propose three novel high-performing BERT-based systems that resolve the annotated medication change characteristics. We demonstrate that our proposed architectures improve medication change classification performance over the initial work exploring CMED. We identify medication mentions with high performance at 0.959 F1, and our proposed systems classify medication changes and their attributes at an overall average of 0.827 F1.


page 1

page 2

page 3

page 4


Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing

Clinical notes, which can be embedded into electronic medical records, d...

Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives

Understanding medication events in clinical narratives is essential to a...

What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

Decision support systems based on clinical notes have the potential to i...

Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework

Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much ...

Change Matters: Medication Change Prediction with Recurrent Residual Networks

Deep learning is revolutionizing predictive healthcare, including recomm...

Mind the Performance Gap: Examining Dataset Shift During Prospective Validation

Once integrated into clinical care, patient risk stratification models m...

Please sign up or login with your details

Forgot password? Click here to reset