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Karimi esair2015

Sarvnaz Karimi
Sarvnaz Karimi

We introduce CADEminer, a system that mines consumer reviews on medications in order to facilitate discovery of drug side effects that may not have been identified in clinical trials. CADEminer utilises search and natural language processing techniques to (a) extract mentions of side effects, and other relevant concepts such as drug names and diseases in reviews; (b) normalise the extracted mentions to their unified representation in ontologies such as SNOMED CT and MedDRA; (c) identify relationships between extracted concepts, such as a drug caused a side effect; (d) search in authoritative lists of known drug side effects to identify whether or not the extracted side effects are new and therefore require further investigation; and finally (e) provide statistics and visualisation of the data. http://dl.acm.org/citation.cfm?id=2810143 際際滷s contain images that have been taken from the web.

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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
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 .
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 |
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 |
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 |
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
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
Searching for possible signals Aggregate all the information found on each drug CADEminer| Sarvnaz Karimi8 |
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 |
CADEMiner architecture CADEminer| Sarvnaz Karimi10 |
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

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Karimi esair2015

  • 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 |
  • 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