Adverse Drug Reaction Detection

Automatically detect Adverse Drug Reactions or Events (ADR / ADE) from multichannel unstructured data, notes, transcripts and literature

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Peer-reviewed State-of-the-art Accuracy

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Auto-detect and Extract Key Facts of Adverse Drug Reactions at Scale

Live Demo
Python Notebook

01

Collect

Multichannel unstructured data
Transcription of calls pharmacist, doctors, patients, and medical affairs
CRM notes, customer support notes
Clinical notes from EMR’s or PDF’s
Social media posts
Biomedical literature

02

Filter

Search
Pre-processing

03

Classify & Extract

Text Classification: does this text describe an adverse event?
Entity Recognition: Identity and normalize the drugs and symptoms
Relation Extraction: Which symptoms are related to which drugs?

We have established a new state-of-the-art accuracy:

ADR and Drug entity extraction
Relation Extraction (RE) models when enriched with a supplementary dataset

Text classification model, for deciding if a conversation includes an ADR

Available as a software or fully managed solution

Data
Collection

we build the data integration pipelines

batch or streaming data
structured, unstructured, or image/PDF files
data quality testing

Information
Extraction

we build the NLP pipelines

text classification
entity recognition
relation extraction

Ongoing
monitoring & Turing

we ensure uptime, performance and accuracy

model tuning
model retraining over time
regular service and software upgrades

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