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Keyword-based search for text, images, and PDFs in the NLP Lab
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Dia Trambitas
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Adverse Drug Reaction Detection
Automatically detect Adverse Drug Reactions or Events (ADR / ADE) from multichannel unstructured data, notes, transcripts and literature
# 1
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
Read the paper
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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