Karimi esair2015

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CADEminer: A System for Mining Consumer Reports on Adverse Drug Side Effects

Sarvnaz KarimiAlejandro Metke-JimenezAnthony 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 don’t know yet!]

CADEminer | Sarvnaz Karimi

Medications & adverse reactions comein 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: 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


Concept extraction


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


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 AMT67% for other concepts

Searching for possible signals

Aggregate all the information found on each drug


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 architecture


www.data61.csiro.au

Sarvnaz Karimi

Research Scientist

t +61 2 9372 4353e [email protected] www.data61.csiro.au

Thank you

Technology

Karimi esair2015