Karimi esair2015
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CadeMiner is a system developed to mine consumer reports from medical forums to identify adverse drug side effects and generate safety signals. The traditional approach to safety signal detection has many drawbacks, including severe under-reporting and barriers to reporting, which CadeMiner aims to address through scalable and public data sources. The system employs methods such as concept extraction and normalisation to filter noise and accurately associate reported side effects with medications.
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Unable to move or feel face
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[We dont know yet!]
CADEminer | Sarvnaz Karimi
Medications & adverse reactions come
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Possible side effects
.
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Possible side effects
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- 1. www.data61.csiro.au CADEminer: A System for Mining Consumer Reports on Adverse Drug Side Effects Sarvnaz Karimi Alejandro Metke-Jimenez Anthony Nguyen October 2015
- 2. May cause Dizziness Blistering, peeling, or loosening of the skin Red, irritated eyes Unable to move or feel face Mental depression May cause [We dont know yet!] CADEminer | Sarvnaz Karimi Medications & adverse reactions come in one package 2 | Possible side effects . Treats . Possible side effects . Treats .
- 3. Safety signal detection Traditional approach: Uses formal reports from pharmaceutical companies, healthcare professionals, or consumers, aggregates them and then decides whether they contain a signal. Drawbacks: Severe under-reporting Difficult to detect early signals Barriers of entry (how/where to report) CADEminer| Sarvnaz Karimi3 |
- 4. Safety signal detection using social media Sharing information through medical forums is Public & interactive Popular: Side effects and other experiences with drugs are commonly discussed Scalable: Several orders of magnitude higher than formal reporting Low barrier to entry CADEminer| Sarvnaz Karimi4 |
- 5. CADEminer: CSIRO Adverse Drug Event miner Goal: A system to mine drug user reviews from medical forums for reports of side effects in order to assist in generating safety signals. Challenge: Filtering the noisy text to find relevant side effects and context where the side effect occurred for a given drug Unsolved problem: How to generate a safety signal from such a low quality data CADEminer| Sarvnaz Karimi5 |
- 6. Concept extraction CADEminer| Sarvnaz Karimi6 | Identify spans of text that mention drugs adverse events or side effects diseases symptoms findings Method: CRFs Accuracy: 98% for drug names and 90% for the rest on CADEC dataset of 1250 forum posts Forum: Askapatient
- 7. Concept normalisation CADEminer| Sarvnaz Karimi7 | Normalise the extracted concepts to a standard form in AMT, SNOMED CT or MedDRA. Demerol/Pethidine/Meperidine Pethidine (AMT) Hunger pangs Pain hunger (MedDRA) Method: Used Ontoserver & in-house mapping of SNOMED CT to MedDRA Accuracy: 92% for drugs to AMT 67% for other concepts
- 8. Searching for possible signals Aggregate all the information found on each drug CADEminer| Sarvnaz Karimi8 |
- 9. Aggregation and matching methods Filtering: Relation extraction to correctly associate a side effect to a drug Aggregation: Pre-processing techniques (spell checking, stemming and stopping) Heuristic rules (e.g., charley horse muscle cramp) Those concepts that are normalized to the same SNOMED CT/MedDRA entries are grouped together (e.g., vomiting and throw up) Matching: Search in drug product information evaluation: MRR 0.34 CADEminer| Sarvnaz Karimi9 |
- 10. CADEMiner architecture CADEminer| Sarvnaz Karimi10 |
- 11. www.data61.csiro.au Sarvnaz Karimi Research Scientist t +61 2 9372 4353 e sarvnaz.karimi@csiro.au w www.data61.csiro.au Thank you