Your SlideShare is downloading. ×
Social media and it's use in disease surveillance
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Introducing the official SlideShare app

Stunning, full-screen experience for iPhone and Android

Text the download link to your phone

Standard text messaging rates apply

Social media and it's use in disease surveillance

363
views

Published on

Published in: Technology, Business

0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
363
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
5
Comments
0
Likes
2
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • Transcript

    • 1. Social media and its use in disease surveillanceMarch 2010
    • 2. ✤ How do we improve disease surveillance?✤ Can social media (e.g. twitter) be effectively used to monitor disease outbreaks?
    • 3. Tweets: disease reports✤ Omg.. The never-ending flu+sore throat.. ☹ bleh.. ☹✤ Stomach flu. Urgh.✤ i love puking... f@#k you flu✤ Having a sore throat,sucks.Having flu,sucks even MORE.DAMMIT!✤ Feeling dizzy/ feverish ever since that class at the gym! overexertion or the flu??
    • 4. Tweets: non disease reports✤ Study finds H1N1 flu in pregnancy is critical risk - Reuters - http://bit.ly/bLiLnz✤ This March Madness turns out to be the flu!✤ Smiling is infectious, You can catch it like the flu. Someone smiled at me today, And I started smiling too.
    • 5. We need Natural LanguageProcessing (NLP)✤ We need a NLP engine in order to process tweets:✤ Tweet → NLP Engine → Its the flu!
    • 6. Maybe we need NLP + Ontologies✤ Do we just search for simple keywords?✤ An ontology can provide us with organized concepts relevant to a domain (i.e. health, biomedicine)✤ How about processing natural language to match concepts organized in an ontology?
    • 7. Ontologies help answer thesequestions✤ How do we know if a user is referring to a symptom or a disease?✤ We seem to need a set of keywords. Where do get this set of symptoms and disease names?✤ How do we link references to one or more symptom to a specific disease?
    • 8. The UMLS Ontology✤ A comprehensive thesaurus and ontology of biomedical concepts✤ Facilitates development of computer systems that behave as if they "understand" the meaning of the language of biomedicine and health.✤ Integrates 2+ million names for ~900k concepts from 60+ families of biomedical vocabularies, and 12 million relations among these concepts.
    • 9. UMLS & MetaMap✤ MetaMap is a tool that given an arbitrary piece of text, finds and returns the relevant concepts available in the UMLS Ontology✤ MetaMap is a software interface to query the “MetaThesaurus” and the “Semantic Network”, both a component of UMLS
    • 10. Concept mapping with MetaMap✤ Using MetaMap to query the MetaThesaurus, we can map the following text strings to the concept "Atrial Fibrillation" ✤ Atrial fibrillation! ✤ AF! ✤ AFib! ✤ Atrial fibrillation (disorder)
    • 11. ✤ But who actually tweets “atrial fibrillation” ??
    • 12. “Having a sore throat, sucks.Having flu, sucks even MORE”✤ Matches: ✤ SORETHROAT (Sore Throat) [Sign or Symptom] ✤ Flu (Influenza) [Disease or Syndrome] ✤ Sucking [Physiologic Function]
    • 13. “i love puking... damn you flu”✤ Matches: ✤ I (Iodides) [Inorganic Chemical] ✤ Love [Mental Process] ✤ Flu (Influenza) [Disease or Syndrome]
    • 14. “Feeling dizzy/ feverish ever since that class atthe gym! overexertion or the flu??”✤ Matches: ✤ Feeling dizzy [Sign or Symptom] ✤ Feverish (Fever) [Finding] ✤ Overexertion (Exhaustion due to excessive exertion) [Injury or Poisoning] ✤ Flu (Influenza) [Disease or Syndrome]
    • 15. “Smiling is infectious, u can catch it like theflu; someone smiled at me today, and I startedsmiling too”✤ Matches: ✤ Smiling [Social Behavior] ✤ Infection [Disease or Syndrome] ✤ Catch (Catch - Finding of sensory dimension of pain) [Sign or Symptom] ✤ Flu (Influenza) [Disease or Syndrome] ✤ Today [Temporal Concept]
    • 16. ✤ Not the best results but it’s a start...
    • 17. Using MetaMap✤ Free of Charge!✤ MetaMap Transfer (MMTx) is a java-based distributable version of the MetaMap program✤ Requires 7GB disk space (uncompressed) and at least 1GB of RAM (2GB recommended)✤ “MetaMap is not an end user product. Users will need a moderate amount of programming knowledge to use MMTx effectively.” - from UMLS website
    • 18. We identified tweets that mentiona concept...SO WHAT?✤ We cant assume its a case report!✤ How the we go around this?✤ Are we done here?
    • 19. Supervised learning to improvethe results?✤ What if we use machine learning?✤ Supervised learning is a machine learning technique for deducing a function from training data
    • 20. Is it feasible?✤ Weka is a collection of machine learning algorithms for data mining tasks.✤ Algorithms can be applied directly to a dataset or called from your own Java code.✤ Input: dataset of concept matches; Output: Classifier Java Class✤ This automatically generated java class can be easily be used to answer if a tweet matching X and Y medical concepts is or is not a disease report
    • 21. Processing a tweet overview✤ Get Tweet✤ Process tweet using MetaMap✤ Get matching concepts from MetaMap✤ Feed the matches to the Classifier Java Class✤ Get a True or False answer indicator “its a disease report”