An Exploratory Study on Pseudo-Data Generation in Prescription and Adverse Drug Reaction Extraction.

Prescription information and adverse drug reactions (ADR) are two components of detailed medication instructions that can benefit many aspects of clinical research. Automatic extraction of this information from free-text narratives via Information Extraction (IE) can open it up to downstream uses. IE is commonly tackled by supervised Natural Language Processing (NLP) systems which rely on annotated training data.

FABLE: A Semi-Supervised Prescription Information Extraction System.

Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are crucial for patients' well-being and is often detailed in the narrative portions of EHRs. As a result, narratives of EHRs need to be processed with natural language processing (NLP) methods that can extract medication and prescription information from free text.

Prescription extraction using CRFs and word embeddings.

In medical practices, doctors detail patients' care plan via discharge summaries written in the form of unstructured free texts, which among the others contain medication names and prescription information. Extracting prescriptions from discharge summaries is challenging due to the way these documents are written. Handwritten rules and medical gazetteers have proven to be useful for this purpose but come with limitations on performance, scalability, and generalizability.